The protection and enhancement of biodiversity are recognized internationally as integral processes for sustainable woodland management. Accurate monitoring is required to assess the effectiveness of forest management approaches directed at conservation of biodiversity. However, traditional biodiversity assessments are costly and time consuming, and rely on high-levels of taxonomic expertise. We propose to harness exciting next generation DNA sequencing methods that have recently revolutionised the amount of high quality reads that can be obtained relatively cheaply to develop a more efficient approach to characterizing forest biodiversity. We intend to use pooled arthropod samples to achieve species identification via a metabarcoding approach in oak woodlands.
One aim of the project is to work towards developing a rapid, inexpensive, fully auditable and standardised prototype method to assess levels of woodland biodiversity and as an efficient tool for biodiversity management and ecological research. Subsequently, the student will utilize this novel approach to address cutting-edge questions relevant to the effects of habitat modification and climate change on patterns of native biodiversity and changes in community assembly processes. The student will work closely with Forestry Commission staff to set up a UK-wide biodiversity monitoring network in semi-natural and plantation woodlands, and will also have the opportunity to address his or her biodiversity-related research questions in a controlled experimental setting, taking advantage of long-term experimental forest plots. The student will also have the opportunity to learn GIS-based analysis and apply predictive modelling to the data to help inform future land management plans under ongoing environmental change.
The student will be supervised by Dr. Lesley Lancaster at the University of Aberdeen and Dr. Nadia Barsoum at the CASE partner organisation, Forest Research (FR), with further support from Professor David Johnson (University of Aberdeen) and Dr. Joan Cottrell (FR). The successful candidate will join the active team of academics and research scientists at the University of Aberdeen, receiving training in scientific writing, data analysis, experimental design, DNA sequencing methodology and bioinformatics. The student will also have the opportunity to gain experience in demonstrating and teaching as part of their academic training. The student will additionally benefit from working in partnership with researchers at an applied research organisation (FR) which tackles questions relating to a broad range of topics relevant to forest practice, management and policy. The student will be encouraged to attend and contribute to national and international scientific conferences during the course of their study.
We are looking for a motivated student who is keen to capitalize on this opportunity to develop his or her ideas, skills, and research independence in a highly supportive environment. A full driving license and a commitment to spend periods of several weeks away from Aberdeen in the field and at Forestry Commission research stations are essential. In the application statement, the student should describe the basis of their interest in the project and detail all relevant skills, qualifications, and prior experiences. The start date for the post will be 1st October 2016.
Please follow the online instructions for the application process, ensuring that you provide an application statement and a C.V. containing the email addresses of your academic or professional references. Please also provide a copy of the degree certificate and transcript for each previous degree undertaken and a copy of your English language proficiency certificate (if relevant).
Note that it is critical that you apply for admission to the ’Degree of Doctor of Philosophy in Biological Science’ to ensure that your application is passed to the correct college for processing.
Yu, D.W., Ji, Y., Emerson, B.C., Wang, X., Ye, C., Yang, C. and Ding, Z. (2012). Biodiversity soup: metabarcoding of arthropods for rapid assessment and biomonitoring. Methods in Ecology and Evolution, 3(4): 613-623
Ji, Y., Ashton, L., Pedley, S., Edwards, D., Tang, Y., Nakamura, A., Kitching, R., Dolman, P., Woodcock, P., Edwards, F., Larsen, T., Hsu, W., Benedick, S., Hamer, K., Wilcove, D., Bruce, C., Xiaoyang, W., Levu, T., Lott, M., Emerson, B. and Yu, D. (2013). Reliable, comprehensive, and efficient monitoring of biodiversity via metabarcoding. Ecology Letters, 16(10): 1245-1257