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Transformative approaches for the monitoring and conservation of biodiversity


Project Description

Human health and wildlife conservation are intimately linked to environmental quality. For example, 9 million premature deaths have been directly linked to environmental change (EC); 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. Biodiversity, which is declining at 1,000 times the natural rate, 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. Biodiversity is impacted by the synergistic action of climate and other environmental factors 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 years.

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, 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. 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.

Funding Notes

CENTA studentships are for 3.5 years and are funded by the Natural Environment Research Council (NERC). In addition to the full payment of their tuition fees, successful candidates will receive the following financial support.
• Annual stipend, set at £15,009 for 2019/20
• Research training support grant (RTSG) of £8,000

References

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2 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. Pollution, health, and the planet: time for decisive action. (Lancet, 2017).
3 Commission, E. The eu Biodiversity Strategy to 2020. (Publications Office of the European Union, Luxembourg, 2011).
4 Sanchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv 232 8–27 (2019).
5 Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol Evol 27, 19-26, doi:10.1016/j.tree.2011.08.006 (2012).
6 Sinha, E., Michalak, A. M. & Balaji, V. Eutrophication will increase during the 21st century as a result of precipitation changes. Science 357, 405-408 (2017).
7 Baert, J. M., Janssen, C. R., Sabbe, K. & De Laender, F. Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions. Nat Commun 7, 12486, doi:10.1038/ncomms12486 (2016).
8 Kanno, J. Introduction to the concept of signal toxicity. J Toxicol Sci 41, SP105-SP109, doi:10.2131/jts.41.SP105 (2016).
9 Nogues-Bravo, D. et al. Cracking the Code of Biodiversity Responses to Past Climate Change. Trends Ecol Evol 33, 765-776, doi:10.1016/j.tree.2018.07.005 (2018).
10 Orsini, L. et al. The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments. Trends in Ecology and Evolution 28, 274-282 (2013).
11 Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335-336, doi:10.1038/nmeth.f.303 (2010).
12 Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. Peerj 4, e2584, doi:10.7717/peerj.2584 (2016).
13 Westcott, S. L. & Schloss, P. D. OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units. mSphere 2, doi:10.1128/mSphereDirect.00073-17 (2017).

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