Overview

Project Highlights:

  • Multidisciplinarity - This project combines high throughput sequencing technology applied to environmental DNA, environmental chemistry, paleoecology, bioinformatics and advanced computational tools
  • Big data science – This project uses advanced computational tools and biostatistics to: 1) help identifying molecular taxonomic units (MOTUs) that decline because of environmental change, which should be prioritized in conservation efforts, 2) help identifying environmental pollutants that should be prioritized in policy interventions based on their adverse effect on biodiversity
  • 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 conservation practice

This project develops revolutionary platforms to enable a comprehensive and cost-effective biodiversity and pollution monitoring of natural ecosystems.

Human health and wildlife conservation are intimately linked to environmental quality1. For example, 9 million premature deaths have been directly linked to environmental change (EC)2; despite efforts to date from regulators and scientists, only 17% of the protected habitats and species under the EU Habitats Directive are in a favourable conservation status at the EU-biogeographical level 3. Biodiversity, which is declining at 1,000 times the natural rate4, is at the core of environmental quality because is the foundation of healthy ecosystems and of the services they provide, which underpin economic prosperity, social well-being and quality of life 5. Biodiversity is impacted by the synergistic action of climate and other environmental factors 6 and its response to such factors varies dramatically in space and time. Differences in environmental sensitivity, biotic interactions, and ecological trade-offs are all context-dependent outcomes from processes operating over many years7.

Here, we propose the development and validation of a unique multi-tiered approach to transform the monitoring of biodiversity and pollution with the long-term goals of improving environmental health. Measures of EC impact have typically been based on dose-effect studies of individual pollutants 8, whereas biodiversity monitoring relies on low throughput, costly approaches that require specialist skills e.g. through light microscopy. These approaches are inadequate and inaccurate because the links between healthy environments and healthy humans are dynamic and complex. Moreover, rapid and cost-effective screening of biodiversity is needed for large scale surveys. We propose a proof of concept to establish a platform for the long-term screening of biodiversity. We propose to apply state-of-the-art technologies in DNA sequencing and mass spectrometry on dated sedimentary archives of natural lakes to discover biodiversity attributes critically impacted by anthropogenic change. Sedimentary archives from inland waters have the unique advantage of preserving temporal biological and environmental signals, allowing the reconstruction of multidecadal dynamics across space 9,10. Working with the UK Environment Agency (UKEA), which co-designed the project and will co-supervise the DR, we aim to implement the optimized platform into environmental practice and monitoring.

 

Changes in species  diversity and abudance through time will be quantified via eDNA and regressed on changes in nutrients levels and climate variables over time in biological archives

Methodology

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. Primary producers (diatoms) and primary consumers (zooplankton) will be quantified using traditional microscopy on a small subset of samples to validate the performance of high throughput screening over established methods.

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 he/she 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 traditional paleoecology via placements at Loughborough University (Ryves), to cutting-edge high throughput sequencing and mass spectrometry at the Environmental Omics and Mass spectrometry facilities, led by Orsini and Abdallah, respectively. The DR will learn bioinformatics (Derelle) 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.

 

Timeline

Year 1: eDNA analysis, including DNA extraction from sediment, ‘amplicon libraries’ preparation and sequencing. Paleoecological analysis of a subset of sediment samples, including the identification of algae and invertebrate remains. Gain familiarity with bioinformatics. Student conference in Birmingham.

Year 2: Mass spectrometry analysis of chemical pollutants in 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.

Year 3: Bioinformatics analysis using QIIME 11,VSEARCH 12, and OptiClust 13. 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.

 

Partners and collaboration (including CASE)

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.

Further Details

This project has been selected as a CENTA Flagship project. This is based on the projects fulfilment of specific characteristics e.g., NERC 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 a potential applicant co-development of the project.

 

For inquiries please contact l.orsini@bham.ac.uk