Ecological effects arising from shipping pressures have generally only been qualitatively described, and then only for individual pressures, not collectively. This project aims to model the current collective effects of shipping-related pressures on the marine biosphere, and how these may change under different IPCC Shared Socioeconomic Pathways (SSP) [1], and corresponding GHG-driven climate change outcomes. The platform for modelling these effects will be the StrathE2E marine ecosystem model [2] which already includes mechanisms for incorporating external pressures such as those arising from shipping and climate change.

Maritime shipping accounts for 80% of the worldwide transportation of traded goods, and is fundamental to global supply chains, passenger and vehicle sea-crossings, and elements of global tourism. However, in coastal zones with high densities of shipping, emissions may present human health hazards, and deposition may increase eutrophication and acidification of marine and terrestrial waterways [3]. Pressure to decarbonise the global economy has led to a surge in attention to engineering solutions for reducing exhaust emissions, such as scrubber systems, carbon capture technologies onboard ships, novel fuels (e.g. ammonia cells), ship hull optimisation and more efficient propulsion [4]. At the same time, there has been increasing attention to the range of other pressures on the marine biosphere arising from shipping [5], their magnitudes relative to other sectors such as fishing and offshore energy generation, and to how they might be managed as part of an ecosystem approach.

If successful in your application, you will complete a programme of doctoral research:

• review existing classifications (e.g. container ships, cruise ships) and activity measures for the shipping sector, how these map onto pressures, and the extent to which pressures may change given projected future engineering innovations to reduce carbon emissions.
• access existing analyses of AIS (Automatic Identification System) data on shipping categories, and/or conduct de-novo analyses to estimate activity rates and the consequent ecosystem pressures in a range of regional sea areas corresponding to existing StrathE2E marine ecosystem models.
• review the few existing projections of shipping activity and attributes under SSP scenarios for shelf sea regions. Build and train a machine learning algorithm to predict shipping activity rates given the high-level SSP narratives.
• document the relationships between shipping pressures and the parameters of ecological processes in StrathE2E. Build a shipping model which will translate the activity rates of shipping categories into data inputs to StrathE2E.
• conduct scenario experiments with StrathE2E in different regional seas to assess the impact of shipping on ecosystem health, and its magnitude relative to other sectors (esp. fishing). Explore the sensitivity of shipping impacts on these ecosystems to SSP and climate scenarios.

You will be registered as a student in the Department of Mathematics and Statistics at the University of Strathclyde, where Dr Laverick and Professor Heath are based. Co-supervisor Prof Lazakis is based in the Department of Naval Architecture, Ocean & Marine Engineering. You will also be supervised by Dr Gordon Hastie (St Andrews), Dr Robert Thorpe (Centre for Environment, Fisheries and Aquaculture Science), Professor Mary Wisz (Danmarks Tekniske Universitet).

Further information

The start date for this opportunity is 6 October 2025 and part time study is an option.

Training

You will: have access to training funds which can be spent to support professional development, attend meetings and conferences, and the acquisition of technical skills. In addition you will complete the Strathclyde PGCert in Researcher Professional Development as well as receive training in coding, systematic literature review, and the mathematical modelling of populations and ecosystems.

Background reading 

• Kramel, D., et al. 2024. Advancing SSP‑aligned scenarios of shipping toward 2050. Nature Scientific Reports (2024) 14:8965.  https://doi.org/10.1038/s41598-024-58970-3
•  Heath, M.R., et al. 2021. StrathE2E: an R package for modelling the dynamics of marine food webs and fisheries. Methods in Ecology and Evolution 12, 280-287. https://doi.org/10.1111/2041-210X.13510.
• Jägerbrand, A.K., et al.. 2019. A review on the environmental impacts of shipping on aquatic and nearshore ecosystems. Science of the Total Environment 695 (2019) 133637
• Moon, H.S., et al.. 2024. Exploring the cost and emissions impacts, feasibility and scalability of battery electric ships. Nature Energy  https://doi.org/10.1038/s41560-024-01655-y
• ICES. 2021. Working Group on Shipping Impacts in the Marine Environment (WGSHIP). ICES Scientific Reports. 3:118. 30 pp. https://doi.org/10.17895/ices.pub.967
• Rodrigues, A.R., et al.. 2023. Integrated ecosystem assessment around islands of the tropical South Mid-Atlantic Ridge. Frontiers in Marine Science 10:1001676. doi: 10.3389/fmars.2023.1001676
• IRENA (2021), A pathway to decarbonise the shipping sector by 2050, International Renewable Energy Agency, Abu Dhabi. ISBN 978-92-9260-330-4. 118 pp.
• Scarbrough, T. et al. 2017.  A review of the NAEI shipping emissions methodology. Final report . Report for the Department for Business, Energy & Industrial Strategy. PO number 1109088. Ricardo Energy &Environment. 102pp.
• ApSimon, H., et al. 2019 (updated Feb 2021). The contribution of shipping emissions to pollutant concentrations and nitrogen deposition across the UK. Contract Report. Defra Contract ECM_53210: Support for National Air Pollution Control Strategies (SNAPCS) 2018-2020 (lot 1)
• Heath, M.R., et al. 2021. Ecosystem approach to harvesting in the Arctic: walking the tightrope between exploitation and conservation in the Barents Sea. Ambio, Changing Arctic Ocean Special Issue, 15pp. 12pp. Published online 3 September 2021, doi.org/10.1007/s13280-021-01616-9
• Torres-Carrión, P.C., et al. 2018.  Methodology for Systematic Literature Review applied to Engineering and Education. IEEE Global Engineering Education Conference (EDUCON) 7-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain, pp1370-1379. 
• Razali, N.A.M., et al. 2021. Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning. Journal of Big Data 8:152. doi.org/10.1186/s40537-021-00542-7
• Zhou, X., et al. 2020. Using Deep Learning to Forecast Maritime Vessel Flows. Sensors 20, 1761; doi:10.3390/s20061761
• Stamer, V. 2021. Thinking outside the container: A machine learning approach to forecasting trade flows, Kiel Working Paper, No. 2179, Kiel Institute for the World Economy (IfW), Kiel. 37pp.
• Wang, T., et al. 2019. A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region. Journal of Marine Science and Engineering 7, 463; doi:10.3390/jmse7120463
• Blair, H.B., et al. 2016. Evidence for ship noise impacts on humpback whale foraging behaviour. Biology Letters 12: 1–5.