The biodiversity of the UK is the most depleted in Europe. Just 7% of remaining forests are thought to be in good ecological condition. However, this figures disguises how little data we actually have to assess biodiversity. Ambitions to expand natural habitats and reverse decades of decline in biodiversity and environmental quality are welcome, but how will we know if our actions have been successful, let alone efficient? More data is required, of higher quality, and we need to make use of it at scale!
One approach is to train statistical models that combine detailed ground surveys with remotely sensed data from drones and satellites to predict ecological condition at any location and support decisions at any scale. We will work with Forestry England to identify which dimensions of forest structure and composition benefit which species, with a particular focus on high value forests within the national estate. To generate bigger and better biological datasets quickly you will use DNA metabarcoding to identify the insect diversity of the forest, and automated acoustic monitoring to record bird and bat activity. By integrating the contrasting and yet complementary observations of each technology this project promises to transform how we perceive and mange biodiversity at large scales.
To support such an exciting project the supervisory team includes Lancaster, Bangor and CEH to bring expertise in remote sensing, genomics and acoustic monitoring. Specific training in molecular ecology and bioinformatics will be provided, as well as data management, science communication and spatial modelling. Field sites will be based in the UK, but there will be opportunities for travel and collaborations in Europe. The successful candidate will become a highly skilled, interdisciplinary graduate working at the interface of multiple advanced ecological techniques and ideally prepared to assist with the drive to renew biodiversity in the UK.
Eligibility
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental or Natural Sciences and applicants with first class degrees and/or high quality Masters qualifications are particularly encouraged to apply. Familiarity with the R or Python programming environment would be an advantage.
Enquiries
To find out more about the project in the first instance please contact Dr. Alex Bush
To apply for this project follow this link
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