Multi-scale ocean response to a large tidal stream turbine array

Dominicis, Michela De; Murray, Rory O’Hara; Wolf, Judith

doi:10.1016/j.renene.2017.07.058

Cited by 67 publications

(56 citation statements)

References 64 publications

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“…Wave conditions were acquired from the ERA-Interim reanalysis (Dee et al, 2011). Significant wave height, mean wave period and mean wave direction were downloaded from the ECMWF website at http://www.ecmwf.int/en/research/ climate-reanalysis/.…”

Section: Carbon and Nitrogen Contentmentioning

confidence: 99%

A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

Wilson

Speirs

Sabatino

et al. 2018

Earth Syst. Sci. Data

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Abstract. Seabed sediment mapping is important for a wide range of marine policy, planning and scientific issues, and there has been considerable national and international investment around the world in the collation and synthesis of sediment datasets. However, in Europe at least, much of this effort has been directed towards seabed classification and mapping of discrete habitats. Scientific users often have to resort to reverse engineering these classifications to recover continuous variables, such as mud content and median grain size, that are required for many ecological and biophysical studies. Here we present a new set of 0.125 • by 0.125 • resolution synthetic maps of continuous properties of the north-west European sedimentary environment, extending from the Bay of Biscay to the northern limits of the North Sea and the Faroe Islands. The maps are a blend of gridded survey data, statistically modelled values based on distributions of bed shear stress due to tidal currents and waves, and bathymetric properties. Recent work has shown that statistical models can predict sediment composition in British waters and the North Sea with high accuracy, and here we extend this to the entire shelf and to the mapping of other key seabed parameters. The maps include percentage compositions of mud, sand and gravel; porosity and permeability; median grain size of the whole sediment and of the sand and the gravel fractions; carbon and nitrogen content of sediments; percentage of seabed area covered by rock; mean and maximum depth-averaged tidal velocity and wave orbital velocity at the seabed; and mean monthly natural disturbance rates. A number of applications for these maps exist, including species distribution modelling and the more accurate representation of sea-floor biogeochemistry in ecosystem models. The data products are available from https://doi.org/10.15129/1e27b806-1eae-494d-83b5-a5f4792c46fc.

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Section: Carbon and Nitrogen Contentmentioning

confidence: 99%

A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

Wilson

Speirs

Sabatino

et al. 2018

Earth Syst. Sci. Data

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“…Changes in habitats from MRE development is likely to be a manageable risk as the footprint on the seafloor or in the water column is relatively small [78], and the risks can easily be informed and evaluated on the basis of extensive knowledge of effects from other industries such as oil and gas operations at sea [79], presence of offshore wind towers and platforms [80], effects of cable laying [48], and a wide range of fishing activities. Changes in physical oceanographic systems can be realized by changing water flow around a bottom-mounted or floating turbine, as well as decreasing wave heights and other effects of removing energy from ocean water in the form of MRE generation [81][82][83]. However, the natural variability of the systems is much higher than could possibly be measured for small numbers of devices [84].…”

Section: Discussionmentioning

confidence: 99%

Risk Retirement—Decreasing Uncertainty and Informing Consenting Processes for Marine Renewable Energy Development

Copping

Freeman

Gorton

et al. 2020

JMSE

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Marine renewable energy (MRE) is under development in many coastal nations, adding to the portfolio of low carbon energy sources that power national electricity grids as well as off-grid uses in isolated areas and at sea. Progress in establishing the MRE industry, largely wave and tidal energy, has been slowed in part due to uncertainty about environmental risks of these devices, including harm to marine animals and habitats, and the associated concerns of regulators and stakeholders. A process for risk retirement was developed to organize and apply knowledge in a strategic manner that considered whether specific environmental effects are likely to cause harm. The risk retirement process was tested against two key MRE stressors: effects of underwater noise from operational MRE devices on marine animals, and effects of electromagnetic fields from MRE electrical export cables on marine animals. The effects of installation of MRE devices were not accounted for in this analysis. Applying the risk retirement process could decrease the need for costly investigations of each potential effect at every new MRE project site and help move the industry beyond current barriers.

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“…Data on six biologic and physical environmental variables have been provided from runs of the NEMO‐ERSEM 3D‐coupled hydrodynamic‐ecosystem model (Edwards, Barciela, & Butenschön, ; O'Dea et al., ; Wakelin, Artioli, Butenschön, & Holt, ). These are a subset of variables that are expected to change with both climate change (Holt, Butenschon et al., ; Holt, Hughes et al., ; Wakelin et al., ) and potentially as a consequence of large‐scale energy extraction for renewable energy (De Dominicis et al., ; Van der Molen et al., ) and also that capture key changes in habitats (Figs. S5, S6 in Appendix ): bottom temperature (BT) (°C), maximum chlorophyll_ a (CHL) (mgC/m 3 ), net primary production (NPP) (mgC/m 2 /day), potential energy anomaly (PEA) (J/m 3 ) (which is the energy required to mix the water column completely), depth‐averaged horizontal current speed (SP) (m/s), and depth‐averaged vertical velocity (DVV) (m/day).…”

Section: Data Descriptionmentioning

confidence: 99%

“…We fit these models to the spatial patterns of eight mobile marine competing and predator–prey species: grey and harbor seals, harbor porpoise, common guillemot, black‐legged kittiwake, northern gannet, Atlantic herring and sandeels. The modeling approach used six physical and biologic variables that are predicted to change with climate change (Holt, Butenschon, Wakelin, Artioli, & Allen, ; Holt, Hughes et al., ) and energy extraction (De Dominicis, O'Hara Murray, & Wolf, ; Van der Molen, Ruardij, & Greenwood, ) and have been considered to be important habitat variables for a range of species: temperature, levels of maximum and cumulative primary production, levels of stratification and aspects of speed, and both horizontal and vertical (Bailey & Thompson, ; Bost et al., ; Carroll et al., ; Schick et al., ; Scott et al., ; Sharples, Scott, & Inall, ). This study sets out to identify which of the bio/physical variables play the most significant role in determining habitat preferences of the selected marine species, defines habitat preferences, measures estimated effect of the bio/physical variables on the selected species, and investigates whether there are common spatial trends for competing and predator–prey species.…”

Section: Introductionmentioning

confidence: 99%

Bayesian joint models with INLA exploring marine mobile predator–prey and competitor species habitat overlap

Sadykova

Scott

Dominicis

et al. 2017

Ecology and Evolution

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Understanding spatial physical habitat selection driven by competition and/or predator-prey interactions of mobile marine species is a fundamental goal of spatial ecology. However, spatial counts or density data for highly mobile animals often (1) include excess zeros, (2) have spatial correlation, and (3) have highly nonlinear relationships with physical habitat variables, which results in the need for complex joint spatial models. In this paper, we test the use of Bayesian hierarchical hurdle and zeroinflated joint models with integrated nested Laplace approximation (INLA), to fit complex joint models to spatial patterns of eight mobile marine species (grey seal, harbor seal, harbor porpoise, common guillemot, black-legged kittiwake, northern gannet, herring, and sandeels). For each joint model, we specified nonlinear smoothed effect of physical habitat covariates and selected either competing species or predatorprey interactions. Out of a range of six ecologically important physical and biologic variables that are predicted to change with climate change and large-scale energy extraction, we identified the most important habitat variables for each species and present the relationships between these bio/physical variables and species distributions. In particular, we found that net primary production played a significant role in determining habitat preferences of all the selected mobile marine species. We have shown that the INLA method is well-suited for modeling spatially correlated data with excessive zeros and is an efficient approach to fit complex joint spatial models with nonlinear effects of covariates. Our approach has demonstrated its ability to define joint habitat selection for both competing and prey-predator species that can be relevant to numerous issues in the management and conservation of mobile marine species. K E Y W O R D SBesag models, bio-physical habitats, hurdle models, integrated nested Laplace approximation, mobile marine species, spatial joint modeling, spatial niche selection, stochastic partial differential equations, zero-inflated models

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Multi-scale ocean response to a large tidal stream turbine array

Cited by 67 publications

References 64 publications

A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

Risk Retirement—Decreasing Uncertainty and Informing Consenting Processes for Marine Renewable Energy Development

Bayesian joint models with INLA exploring marine mobile predator–prey and competitor species habitat overlap

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