Responsible environmental monitoring is fundamental to understanding and protecting marine environments and the sustainable development of marine resources, such as deep-sea mining, offshore oil and gas, or renewables. Long-term environmental monitoring is an essential component of the Global Ocean Observation Strategy and of particular relevance in the current UN Decade of Ocean Science for Sustainable Development. However, monitoring of temporal change in the remote deep sea is rare. Seabed photography provides a feasible, quantitative, repeatable, cost-efficient solution, and is increasingly used in the assessment and monitoring of change in remote marine environments, particularly by industry and government agencies. Effective ocean observation and successful monitoring require comparability between data collected at different time points, so practicable optimization of data collection for interoperability is important; however, these conditions for robust monitoring are not commonly met, and key questions remain.
The aims of this project are to: (1) monitor temporal ecological change to megabenthic communities using towed cameras (and data captured with other camera platforms); and (2) establish best practices for optimizing interoperability of photograph-derived data for monitoring marine seabed communities.
The study will compare seabed megafaunal communities over time. Existing photographic datasets collected over multiple recent expeditions are available, including from the Porcupine Abyssal Plain Sustained Observatory, an abyssal long-term time series site where large swings in megafaunal community structure have been observed. The recent images were collected using a towed camera (HyBIS); towed cameras are a popular, simple seabed photographic platform popular with consultancies and regulatory agencies. We have previous image sets from another towed camera and an autonomous underwater vehicle. Ecological change will be determined (e.g., by assessing density, biomass, diversity and community composition from photos), and effects of the data acquisition on the ecological results will be assessed (e.g., camera/lighting specifications, survey design, annotation strategies). The student will identify best practices for marine imaging for environmental monitoring, with consideration for biological community structure. These optimized approaches will be used to obtain new imagery at these sites (collected during research cruises that the student would have the opportunity to participate in), and generalized as recommendations for UK-wide marine monitoring.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the National Oceanography Centre (Southampton). Specific training will include: techniques for production and interpretation of ecological datasets from marine images, familiarisation with image annotation software, seafarer training for any opportunities to participate in research expeditions during the project, and an internship(s) at JNCC. The internship will provide experience of applied imagery analysis and quality assurance practices to meet monitoring requirements of the UK’s extensive, and growing, network of MPAs (25% UK waters), as well as an understanding of national biodiversity conservation instruments and future management approaches for the UK’s offshore natural capital resources.
Please see https://inspire-dtp.ac.uk/how-apply for details.
[1] Durden, J.M., Schoening, T., Althaus, F., Friedman, A., Garcia, R., Glover, A., Greniert, J., Jacobsen Stout, N., Jones, D.O.B., Jordt-Sedlazeck, A., Kaeli, J.W., Koser, K., Kuhnz, L., Lindsay, D., Morris, K.J., Nattkemper, T.W., Osterloff, J., Ruhl, H.A., Singh, H., Tran, M., Bett, B.J., 2016. Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding. In R.N. Hughes, D.J. Hughes, I.P. Smith, A.C. Dale (Eds.), Oceanography and Marine Biology: An Annual Review, Vol. 54 (pp. 1-72): CRC Press.
[2] Durden, J.M., Bett, B.J., Schoening, T., Morris, K.J., Nattkemper, T.W., and Ruhl, H.A. (2016). Comparison of image annotation data generated by multiple experts for benthic ecology. Marine Ecology Progress Series 552, 61-70. doi: 10.3354/meps11775.
[3] Morris, K.J., Bett, B.J., Durden, J.M., Huvenne, V.A.I., Milligan, R., Jones, D.O.B., McPhail, S., Robert, K., Bailey, D., Ruhl, H.A., 2014. A new method for ecological surveying of the abyss using autonomous underwater vehicle photography. Limnology and Oceanography: Methods, 12, 795-809. 10.4319/lom.2014.12.795
