‘New Normals’ of the High Seas: Assessing the impacts of 14 years of Sargassum invasions on previously unstructured open waters of the Tropical North Atlantic

A PhD project with the University of Plymouth
Aerial view of Sargassum seaweed in Atlantic ocean

Project background

In 2011, a Great Belt of Sargassum seaweed formed in open waters of the tropical Atlantic for the very first time (Wang et al., 2019). This marked the start of notorious reoccurring invasions every year, with seasonal Sargassum blooms big enough to be seen from space. At the distal reach of the Great Belt, recurring golden tides of Sargassum have imposed predominantly negative impacts to coastal communities of the Caribbean and Gulf of Mexico. In open waters, however, large patches of Sargassum are likely to have been (i) supporting development of biodiversity hotspots in the form of a drifting habitat corridor from West Africa to the Caribbean, and (ii) imposing on the physical environment by dissipating wind-wave energy and turbulent mixing in the upper ocean surface layer. 
Evidence of changes to the biology and physics of the tropical North Atlantic are likely to already be apparent, if you know where to look. This multidisciplinary PhD project will be the first to collate a range of existing datasets to assess the impacts of 14 years of seasonally recurring Sargassum blooms on previously unstructured high seas of the Tropical North Atlantic.
Figure 1: Aggregated Sargassum blooms floating in open waters of the Tropical North Atlantic and coastal waters of the Caribbean.
Figure 1: Aggregated Sargassum blooms floating in open waters of the Tropical North Atlantic and coastal waters of the Caribbean.

Project aims and methods

In previously unstructured high seas, what are the impacts of ‘New Normal’ Sargassum blooms on the biology and physics of the Tropical North Atlantic? The purpose of this project will be to answer the question using existing data from satellite-tracked marine animals, earth observation satellites, moored and drifting buoys, and numerical models.
  • Biology: At-sea distributions of marine animals will be sourced from existing tag datasets. Fishing boats will also be utilised as predators, with their positions and activities derived from satellite Automatic Identification System (AIS). Using data acquired by a range of satellite sensors, Sargassum blooms and covariates will be assessed for changes over ~30 years. Distributions of marine animals and fishing fleets will be examined in relation to these biogeochemical and physical variables, with a focus on distribution patterns around the seasonal Sargassum Belt.
  • Physics: Analysis of physical wave data will build on research of wave energy attenuation by sea ice floes (polar oceans) and canopy-forming kelp (coastal environments). Existing data repositories will be accessed, including altimetry-derived wave data from CMEMS at Level-4, and in situ data from moored (NOAA) and drifting (Sofar Ocean) instruments measuring wind, wave height, and temperature. Global numerical wave models such as WAVEWATCH III® will be used to provide wave boundary conditions, additionally allowing for model validation in regions of canopy-forming algae. 
 

Eligibility and candidate requirements

  • We encourage candidates with a strong interest in the marine environment and strengths in data processing and analysis to apply. 
  • Applicants should have a first or upper second-class honours degree in an appropriate field of marine science, and preferably a MSc that shows multidisciplinary skills.
  • Data analysis skills in R or Python (or similar) are essential. 
  • Experience using Linux or Unix-based operating systems is desirable.
  • Any successful applicant to this position will be expected to assist in demonstrations and processing sessions related to sidescan and multibeam hydrographic surveys and CTD and ADCP oceanographic surveys. Instruction and guidance will be provided but sufficient background in numeracy to facilitate that training will be assumed.
 

Student training

This multidisciplinary project is centralised around the analysis of existing ‘big data’ using machine learning and numerical modelling. The supervisory team have expertise covering all aspects of the project, namely: 
  1. marine remote sensing and machine learning, 
  2. wave modelling, hydrodynamics, and wave-current interaction with macroalgae, and 
  3. oceanographic drivers of marine animal habitat-use and behaviour. 
Our student will thus benefit from skills development in bio-logging, satellite remote sensing, machine learning, numerical modelling, in situ data collection, and ‘big data’ analysis.
 

Key recent papers by the supervisory team

Fidai, Y.A., Dash, J., Marsh R., Oxenford H.A., Biermann, L., Martin, N., Tompkins, E.L. (2023). Tracking and Detecting Sargassum pathways across the Tropical Atlantic. Accepted for publication in Environmental Research Communications (November 2023).
Letessier, T.B., Mannocci, L., Goodwin, B., Embling, C., de Vos, A., Anderson, R.C., Ingram, S.N., Rogan, A. and Turvey, S.T. (2023). Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes. Conservation Biology, e14043. 
Lindhart, M., Daly, M. A., Walker, H., Arzeno-Soltero, I. B., Yin, J. Z., Bell, T. W., Monismith, S. G., Pawlak, G., & Leichter, J. J., (in review) Short wave attenuation by a kelp forest canopy, Limnology & Oceanography Letters.
Michelot, T., Glennie, R., Thomas, L., Quick, N., & Harris, C. M. (2023). Continuous-time modelling of behavioural responses in animal movement. The Annals of Applied Statistics, 17(4), 3570-3588.
Valiente, N.G., Saulter, A., Gomez, B., Bunney, C., Li, J.-G., Palmer, T, & Pequignet, C. (2023). The Met Office operational wave forecasting system: the evolution of the Regional and Global models. Geo. Model Dev. Disc. 16, 2515–2538.
If you wish to discuss this project further informally, please email the supervisory team. We’re excited about this project, and we’d be happy to answer any questions you may have.

Supervisory team