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  A year in the microbial life of the Arctic Ocean


   School of Computing Sciences

  , , ,  Wednesday, January 10, 2024  Competition Funded PhD Project (Students Worldwide)

About the Project

Arctic ecosystems are among those that are most affected by climate change anywhere on Earth, with air temperatures rising four times faster than the global average. The Arctic Ocean supports a complex ecosystem including fish, marine mammals, and zooplankton, and microbes are at the base of this food web. However, due to inaccessibility of the Arctic, there is a fundamental lack of understanding into how microbial communities in the Central Arctic Ocean respond to seasonal changes. To fill this knowledge gap, the 2019-2020 Multidisciplinary Observatory for Study of the Arctic Climate (MOSAiC) expedition undertook the largest ever survey of the Arctic Ocean, including the first ever year-long time series of Arctic metagenomes.

Research methodology

The aim of this project is to analyse and model changes in microbial diversity and traits over the course of a year, using genomic data from the MOSAiC expedition whose analysis is being co-coordinated by experts in the supervisory team at UEA. The student will synthesise multiple data sources from MOSAiC, including biogeochemical measurements, oceanographic data, and the MOSAiC metagenome time-series. Gene abundance and expressions level will be used as indicators of physiological traits, such as ice-adaptation and nutrient metabolism. The student will become familiar with both traditional species distribution models and machine learning tools and will use them to build models describing seasonal changes in microbial communities. In latter stages of this project, these models will be combined with broader oceanographic and climate predictions to forecast how Arctic microbial communities and traits might change in response to climate change.

Training

The student will work with a world-leading team of experts in Arctic microbial genomics and ocean modelling based at UEA, The Earlham Institute and University of Bristol as well as other members of the MOSAiC consortium. They will gain new skills in areas including bioinformatics, data analysis, machine learning, ecosystem modelling, and marine microbiology.

Person specification

2:1 Bachelor degree in Computer Science, Data Science or equivalent. We are looking for an enthusiastic student who is excited about applying interdisciplinary techniques to understand the potential effects of global change on oceanic microbes.

Funding notes

This project has been shortlisted for funding by the ARIES NERC DTP.

Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£18,622 p.a. for 2023/24) and research funding. International applicants are eligible for fully-funded ARIES studentships including fees. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK.

Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.

ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage mail%20to: and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside significant relevant non-academic experience.

For further information, please visit www.aries-dtp.ac.uk

Project Code - MOULTON_UCMP24ARIES

Biological Sciences (4) Computer Science (8) Mathematics (25)

Funding Notes

Competition Funded Project
Source of funding: NERC
Studentship length: 3.5 years
Amount of funding:
- Fees: Standard UKRI rates
- Stipend: Standard UKRI rates
- RTSG – To be confirmed

References

1 Mock T, Boulton W, Balmonte JP, Barry K, Bertilsson S, et al. (2022) Multiomics in the central Arctic Ocean for benchmarking biodiversity change. PLOS Biology. 20(10): e3001835. https://doi.org/10.1371/journal.pbio.3001835
2 Duncan A, Barry K, Daum C, et al. (2022) Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans. Microbiome. 10(1):67.
doi:10.1186/s40168-022-01254-7
Duncan A, Barry K, Daum C, et al. (2022) Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans. Microbiome. 10(1):67.
doi:10.1186/s40168-022-01254-7
3 Velten, B., Braunger, J.M., Argelaguet, R. et al. (2022) Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat. Methods. 19, 179–186. https://doi.org/10.1038/s41592-021-01343-9
4 Ovaskainen, O., Tikhonov, G., Norberg, et al. (2017), How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20: 561-576. https://doi.org/10.1111/ele.12757
5 Winder JC, Boulton W, Salamov A, Eggers SL, Metfies K, Moulton V, Mock T. Genetic and Structural Diversity of Prokaryotic Ice-Binding Proteins from the Central Arctic Ocean. Genes. 2023; 14(2):363. https://doi.org/10.3390/genes14020363

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