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Community ecology of deep-sea benthic systems

Supervisor: Dr Emily Mitchell ( - a link to further information on Dr Mitchell will be available shortly)

Co-supervisor: Professor Andrea Manica

Project summary:

The deep-sea hosts a extensive variety of benthic organisms across wide range of habitats, from hydrothermal vents to abyssal planes.  Community composition differs widely within these habitats as well as between them with high beta diversity and variable alpha diversity.  However, relatively little is known about the fine-scale community ecology of deep-sea systems because it is only recently that video and photographic data had the resolution to create accurate reconstructions. The project aims to determine the drivers of community structure of deep-sea benthic communities on fine and large scales using newly developed methodologies for the analysis of community ecology and 3D model reconstruction.   This work will enable us to investigate the drivers behind community composition, and how deep-sea taxa interact with each other and their environment.  By establishing how these communities operate, it will be possible to get a better understand how vulnerable these communities are to changes. This project will investigate how ecological interactions and associations vary between different deep-sea environments including trench, sea mount, abyssal hills and plains, cold seeps and hydrothermal vents.   Fine-scale ecological analyses and network analyses will be used to investigate how these different habitats impact community dynamics.  The student will establish what impact different environmental and temporal effects have deep-sea communities, what the role of body-size and traits are on any differences, and so investigate the driving forces behind ecosystem structure.

What the student will be doing:

The student will use substantial existing photographic and video data from the NOAA deep-sea coral database to reconstruct deep-sea benthic communities across a range of different environments.  The community ecology will be investigated using spatial point process analyses will be used to determine fine-scale habitat associations and competitive and facilitative interactions between species. Bayesian network inference, NMDS cluster analyses and multivariate regression analyses will enable large-scale ecological drivers and trends to be determined.  These analyses will be used to establish how community ecology of these deep-sea systems varies over different environmental and complexity gradients.  Further analyses will investigate the role of organism height and body-size have on dynamics and whether there are any biological “super-traits” (single traits that explain multiple different behaviours, such as colony mass per unit area for corals) which exist to correlate these organisms with their community structure.  The student will be trained in 2D and 3D digital community reconstructions, as well as in Bayesian network inference and spatial point process analyses. The student will be encouraged to apply for relevant external training courses on statistics and spatial modelling, and to attend national and international conferences to develop presentation and networking skills.

Student should have a background in ecology, with experience with coding and statistical analyses.  Knowledge of benthic marine biology is preferred, but not required.  The student should be highly organised and detailed orientated.


  1. Mitchell, E. G., & Butterfield, N. J. (2018). Spatial analyses of Ediacaran communities at Mistaken Point. Paleobiology44(1), 40-57.
  2. Mitchell, E. G., & Kenchington, C. G. (2018). The utility of height for the Ediacaran organisms of Mistaken Point. Nature ecology & evolution, 2(8), 1218.
  3. Rex, M. A., & Etter, R. J. (2010). Deep-sea biodiversity: pattern and scale. Harvard University Press.