Application of novel 3D imaging techniques to quantify biomass and secondary production associated with North Sea artificial structures.

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Anthropogenic structures are deployed in marine environments to support industrial activities worldwide. Sessile epibiota rapidly colonise structures in the sea, in turn attracting mobile invertebrates, fish and top predators. Understanding the ecosystem effects of the increasing number of man-made structures in marine environments is a priority for research, and necessary to support sustainable installation and decommissioning practices worldwide.Secondary production is a measure of energy flow through the food-web, and relates directly to ecosystem function, thus secondary production is a proxy for ecosystem function. In order to understand the relationship between secondary production and wider ecosystem processes (e.g. mobile mega fauna behaviour), we need to accurately predict secondary production (the focus of this proposal), and make this data available to ecosystem modellers. Obtaining bespoke data on secondary production associated with offshore structures is limited by the time/cost constraints of conducting dedicated ecological surveys. Offshore energy operators use remotely operated vehicles (ROVs) to obtain videos of infrastructure for maintenance purposes. These videos cover all structures types, ages and locations. Recent advances in "Structure from Motion Photogrammetry" mean that it is now possible to generate 3D images of epibiota from this video footage, and use the 3D images to estimate the biovolume of epibiota. Biovolume can be converted to biomass, then to secondary production, by applying taxa-specific conversion factors. By pairing 3D imaging with supervised machine learning algorithms to automatically identify taxa, (and then apply the taxa-specific conversions), large volumes of ROV data can be rapidly processed to produce high-resolution estimates of secondary production for entire structures /production basins. In a previous feasibility study, we pioneered 3D imaging of man-made structures in temperate and tropical waters, and used these images to estimate epibiota biovolumes. We have developed and applied protocols to convert biovolumes into biomass via taxon-specific calibration curves. Here, we propose to generate 3D images for 85 man-made structures located in the North Sea, and wider UK waters, using existing ROV footage. From the images, we will estimate the biovolume of the observed taxa. We will then develop/refine machine learning algorithms to automatically identify the taxa within the 3D images, and apply taxa-specific volume-to-mass calibration curves. We will bring these developments together to estimate secondary production on the 85 man-made structures, and develop a statistical model of secondary production as a function of structure location, type and age, which can be applied to other structures. Our novel approach will enable us to (1) generate, for the first time, an estimate of secondary production across all offshore energy structures within the whole North Sea ecosystem,(2) predict changes to ecosystem function stemming from a range of installation/decommissioning scenarios, and (3) cross-validate/compare our estimates to natural reef habitats and structures in Gulf of Mexico, Australia and the Gulf of Thailand, where similar techniques are being applied. Our research, which addresses INSITE2 Challenges 2 and 3, will significantly advance our understanding of the ecological role played by man-made structures, and serve as an evidence base to support local, regional and global assessments of the ecosystem-scale consequences of installing and removing structures. Through development of 3D imaging and auto-ID, we will also deliver a novel monitoring tool that facilitates a strategic whole-system approach to the monitoring/regulation of offshore structures. Such a tool could be readily applied to historic industry data for ecological (and engineering) applications.