- Multidisciplinarity – This project combines high throughput sequencing technology, environmental chemistry, bioinformatics and advanced computational tools
- Big data science – This project uses advanced computational tools and biostatistics to: 1) identify molecular taxonomic units (MOTUs) that delivery ecosystem functions; 2) rank environmental pollutants based on their adverse effect on biodiversity and ecosystem functions. These actions guide conservation and mitigation interventions.
- Link to policy – The co-design and co-supervision of the PhD by the UK Environment Agency ensures the translation and dissemination of intervention mechanisms to transform environmental practice
This project develops a novel framework to provide an in-depth understanding of ecosystem complexities through a comprehensive screening of the spatio-temporal interdependencies of biodiversity, pollution and ecosystem functions.
In the last 50 years, biodiversity has declined at 1,000 time the natural rate (Sanchez-Bayo & Wyckhuys 2019). Rapid and severe biodiversity loss has caused the decline of more than 60% of ecosystem services, including provisioning (food), regulating (e.g. climate), supporting (nutrient cycling, primary production), and cultural (e.g. aesthetic and recreational) services (Mace et al. 2012). Environmental deterioration and loss of ecosystem services are responsible for economic loss, whereas pollution is responsible for the premature death of 9 million people yearly (Landrigan et al. 2017).
Mitigation interventions aimed at conserving and restoring natural capital have so far been inefficient and inadequate. This is in part because research on biodiversity and ecosystem services is constrained by disciplinary boundaries, whereas truly cross-disciplinary solutions are needed (Barnosky et al. 2016). Furthermore, scientific knowledge has rarely been translated into tools for stakeholders with the result that decision-making frameworks that enable the prioritization of intervention mechanisms based on objective evidence simply do not exist (Ehrlich et al. 2012).
Here, we lay the foundation for a unique multi-tiered approach to transform the monitoring of biodiversity and pollution with the long-term goals of improving environmental and human health. To develop such a transformative approach, the combined effect of multiple threats has to be considered, knowing that synergistic effects among environmental threats are responsible for at least 50 and up to 68% of species or community changes (Jackson et al. 2016). Moreover, causes of biodiversity loss can operate on different spatial and temporal scales (Bonebrake et al. 2019) and are all context-dependent outcomes from processes operating over many years (Nogues-Bravo et al. 2018).
Long-term dynamics of biodiversity, abiotic properties, and ecosystem functions will be reconstructed via an unprecedented integration of biochemical and environmental fingerprinting of biological archives spanning centuries. The long-term dynamics are then placed in a machine learning pipeline to identify cause-effect relations between environmental pollution and biodiversity dynamics. The models are tested via hindcasting, and then used to accurately forecast the future of ecosystem services under different climate change scenarios. Working with the UK Environment Agency (UKEA), which co-designed the project and will co-supervise the DR, we aim to provide accessible tools to practitioners to translate cutting-edge research into practical solutions for environmental practice and management.
This is a CENTA Flagship Project
This project is suitable for CASE funding
HostUniversity of Birmingham
- Organisms and Ecosystems
- Dr Luisa Orsini, School of Biosciences, University of Birmingham
- Dr Jiarui Zhou, School of Biosciences, University of Birmingham
- Dr Mohamed Abdallah, School of Geography, Earth and Environmental Science University of Birmingham
- Dr Sami Ullah , School of Geography, Earth and Environmental Science University of Birmingham
- Drs Glenn Watts and Kerry Walsh Environment Agency
Bio-chemical fingerprinting. Metabarcoding or marker gene sequencing will be combined with chemical fingerprinting using advanced mass spectrometry analysis to determine the responses of lake systems to abiotic environmental change and variation in pollutant levels.
Ecosystem Functions analysis. Bulk stoichiometry of sediments (phosphorous: P; nitrogen: N; carbon: C) will be used to elucidate long-term dynamics in productivity as influenced by nutrient availability and the relationships of stoichiometric ratios, productivity and biological attributes.
Bio-chemical associations. Sparse Canonical Correlation Analysis (sCCA) will be employed to regress measures of biodiversity attributes on biotic and abiotic factors in standard, multiple regressions using generalized linear models (GLM). sCCA is ideal for discovering complex, group-wise patterns between high-dimensional datasets.
Translation. To create long-lasting impacts beyond the project, the DR will have placements at the UK EA, during which they will engage with the research team, ensure transfer of knowledge and drive translation of research findings into environmental practice.
Training and skills
The DR will receive multisciplinary training spanning from cutting-edge high throughput sequencing (Orsini), mass spectrometry (Abdallah), and biogeochemistry (Ullah). The DR will learn bioinformatics (Orsini) and advanced computation with focus on machine learning (Zhou), benefiting from access to one of the most up to date high performance computing facilities in the country. The DR will spend up to 6 months at the UK Environment Agency to learn the skills of translational science.
Partners and collaboration
This is a CASE application in which the UKEA offers funding at £1K per year, and placements for the DR in the applied research team at the UKEA. Dr Watts works in the Evidence Directorate of the Environment Agency, leading a research team of 11 specialising in climate change and resource efficiency. Dr Walsh specializes in eDNA as an alternative approach for understanding species presence in freshwater ecosystems. She has been responsible for introducing operational DNA approaches at the UKEA. Both have co-designed the proposed project and will offer co-supervision and training to the DR.
For inquiries please contact firstname.lastname@example.org
Applications need to be submitted via the University of Birmingham postgraduate portal, https://sits.bham.ac.uk/lpages/LES068.htm, by midnight 11.01.2021. Please first check whether the primary supervisor is within Geography, Earth and Environmental Sciences, or in Biosciences, and click on the corresponding PhD program on the application page.
This application should include
- a brief cover letter, CV, and the contact details for at least two referees
- a CENTA application form
- the supervisor and title of the project you are applying for under the Research Information section of the application form.
Referee’s will be invited to submit their references once you submit your application, but we strongly encourage applicants to ensure referees are aware of your submission and expecting a reference request from us. Students are also encouraged to visit and explore the additional information available on the CENTA website.
This is a CENTA Flagship Project
These have been selected because the project meets specific characteristics such as CASE support, collaboration with our CENTA high-level end-users, diversity of the supervisory team, career development of the supervisory team, collaboration with one of our Research Centre Partners (BGS, CEH, NCEO, NCAS) or student co-designed project. These characteristics are a CENTA priority. Studentships associated with Flagship projects will be provided exactly the same level of support as all other studentships.
eDNA analysis, including DNA extraction from sediment, ‘amplicon libraries’ preparation and sequencing. Bulk stoichiometry of sediment to quantify nutrient availability. Gain familiarity with bioinformatics. Student conference in Birmingham.
Mass spectrometry analysis of chemical pollutants and stoichiometry of sediments of sediment. Biostatistics. Prepare draft of first thesis chapter. Placement at the UK Environment Agency to set out a plan for the translation of the biodiversity screening methods into environmental practice.
Bioinformatics analysis using QIIME (Caporaso et al. 2010),VSEARCH (Rognes et al. 2016), and OptiClust (Westcott & Schloss 2017). Application of machine learning algorithms to link biodiversity attributes and environmental changes (e.g. eutrophication and chemicals). Placement at the UK Environment Agency finalized to training UKEA researchers and implementing the new platform into environmental practice. Present thesis work in international conferences. Write thesis. Submit research article.
1.Barnosky, A.D., Ehrlich, P.R. & Hadly, E.A. (2016). Avoiding collapse: Grand challenges for science and society to solve by 2050. Elementa: Science for the Anthropocene, 4, 000094.
2.Bonebrake, T.C., Guo, F., Dingle, C., Baker, D.M., Kitching, R.L. & Ashton, L.A. (2019). Integrating Proximal and Horizon Threats to Biodiversity for Conservation. Trends Ecol Evol, 34, 781-788.
3.Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K. et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 7, 335-336.
4.Ehrlich, P.R., Kareiva, P.M. & Daily, G.C. (2012). Securing natural capital and expanding equity to rescale civilization. Nature, 486, 68-73.
5.Jackson, M.C., Loewen, C.J.G., Vinebrooke, R.D. & Chimimba, C.T. (2016). Net effects of multiple stressors in freshwater ecosystems: a meta-analysis. Global Change Biology, 22, 180-189.
6.Landrigan, P.J., Fuller, R., Acosta, N.J.R., et al. . , p.o.O. & http://dx.doi.org/10.1016/S0140-6736(17)32345-0. (2017). Pollution, health, and the planet: time for decisive action. (ed. health, TLCopa). Lancet.
7.Mace, G.M., Norris, K. & Fitter, A.H. (2012). Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol Evol, 27, 19-26.
8.Nogues-Bravo, D., Rodriguez-Sanchez, F., Orsini, L., de Boer, E., Jansson, R., Morlon, H. et al. (2018). Cracking the Code of Biodiversity Responses to Past Climate Change. Trends Ecol Evol, 33, 765-776.
9.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. (2016). VSEARCH: a versatile open source tool for metagenomics. Peerj, 4, e2584.
10.Sanchez-Bayo, F. & Wyckhuys, K.A.G. (2019). Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv, 232 8–27.
11.Westcott, S.L. & Schloss, P.D. (2017). OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units. mSphere, 2.
The PhD proposal is part of a larger international effort involving other UK and overseas universities. The DR will have access to extant sediment cores at the supervisor’s laboratory, mitigating risks associated with field work and enabling the DR to commence laboratory work promptly. The molecular biology laboratory has containment measures to be operative under covid restrictions. In case of a complete lock down requiring closure of the University, the DR training and activities will focus on the computational aspects of the project. The DR will have access to previously generated pilot data by the main supervisor’s team for this exercise. Under these circumstances, the DR will be guided to write a review and synthesis of the field that will be submitted for publication as key component of the PhD program.