FLOWBEC + RESPONSE

Beth Scott, Eric Armstrong, Chris Hall, Benjamin Williamson, James Waggitt, Philippe Blondel, Paul Bell – and more…

NERC / DEFRA projects on the potential impact of renewables

FLOWBEC

Creating new UPWARD FACING instrument with EK60 multi-frequency + Multibeam sonar to capture prey and seabird behaviour

RESPONSE

DOWNWARD FACING acoustics EK60 multi-frequency + Observersto define seabird foraging habitat.

FLOWBEC & RESPONSE research projects:

• The research in these projects attempts to understand the exact mechanisms involved in how mobile marine predators (fish, birds and mammals) capture their prey. This allows us to understand in detail what sort of physical habitat characteristics these animals need to be present to successfully forage.

• Will be able to predict on a range of spatial scales how renewable developments may effect individuals – and cumulative effects up to population

New Technical Design:FLOWBEC

A top-down view of the frame showing the relative positions of the MBES, MBES control housing and MBES battery bank. The approximate (un-calibrated) mounting orientation of the Scanmar inclination sensor (used during deployment only) and the inclination sensor (logged throughout the

survey) are both shown.

Systems overview of the subsea sonar platform.

Each battery housing contains a pair of 12V 220Ah SLA batteries as shown on the left. The right figure shows the two high current charge lines, the two low current output connectors, the three position charge-off-output switch and the vent plug

Pitch and roll varied by less than 1° over the course of the deployment

Slide by Benjamin Williamson

Slide by Benjamin Williamson

Slide by Benjamin Williamson

Slide by Benjamin Williamson

Slide by Philippe Blondel and Benjamin Williamson

Multi Beam

Ek60 Initial Results

High velocity flow - large turbulence

structures (with echo)

Low velocity flow – fish schools?

Output FLOWBEC :Technical

1. ECUA paper now published on POMA: Williamson, B.J., Blondel, P., Multibeam imaging of the environment around marine renewable energy devices, IOA 11th European Conference on Underwater Acoustics (ECUA 2012), Edinburgh, Scotland, Jul 2012. http://dx.doi.org/10.1121/1.4772810

2. Overview technical design (draft in progress) FLOWBEC – The design and deployment of a self-contained acoustic monitoring platform for mapping the environment around marine renewable energy devices.

3. Processing of MBES data (draft in progress) Multibeam imaging and acoustic analysis of the environment around marine renewable energy devices.

Output FLOWBEC: Ecological Post-doc and PhD – working on cleaning data and defining turbulence/noise signals so to be able to remove [mostly

done] – so can see clearly fish and bird behaviour. 1. Processing of EK60 data (upon BW transfer to Aberdeen)

Multi-frequency echosounder analysis of the environment, fish, diving seabirds and turbulence around marine renewable energy devices.

2. Data integration Integrating multibeam sonar, multi-frequency echosounder, marine X-band radar and bird observation data to map the environment around marine renewable energy devices.

3. New PhD joint with Prof Vladimir Nikora at UoA (fluid dynamic engineer) to use Turbulence data to define turbulence characteristics - for understanding animals usage and potential for design of turbine blades

RESPONSEforaging habitat: how to define?

• Species specific habitat use? (fish, birds and mammals)

• physical characteristics quantified?

• NOTE – results presented in Response preformed by James Waggitt (PhD, Aberdeen)

ALBA Na MaraMarine Scotland Science

Mean numbers vs prevalence(few very high counts) (how often seen in location)

5 years of on-shore survey data

Use of zig-zag survey technique against the tidal flow

• Speed of tide will determine speed over ground and area/ volume of water sampled

• Sampled range of different turbine types and depths

Foraging distributions

(A) all seabirds(B) black guillemots n=231(C) Atlantic puffins n=823 (D) common guillemots

n=275

RESPONSE research projects:

• The surface features that visual animals may use to identify areas of foraging such as roughness, convergent areas, shear lines. Surface features may also bias the visibility of marine animals to observational counts and we are performing analyses to correct for these biases.

A problem of detection in fast flows in rougher water?

Radar: Fine scale flow and 3-D habitat characteristics

(Paul Bell, NOC, POL)

Roughness increases /decreases with tidal speed & direction

Locations of convergence/divergence & shear from MIKE 21 2-D data

EK60 facing downwards from vessel: detects prey and turbulence

Sub-surface Turbulence associated with water boils

(kolks)Fish Shoal

Depth of turbulence at different directions of the tide

Flood Ebb

Difference in turbulence around turbinestop increasing ebb, bottom max flood

Why is it important to Government policy /Developers / Energy Companies?

• can predict physical effects of renewable developments on the oceanographic environment,

• Therefore if we can link animal behaviour to physical aspects of the oceans, we can more accurately predict what (if any) the effects of renewable developments will have on the foraging success of marine animals.

• Foraging success drives population dynamics as it controls adult and juvenile survival.

• Short term, highly focused studies can lead to a rapid increase in our understanding and the research may ultimately allow us to separate the effects of any large scale renewable developments from climate change effects.

Output FLOWBEC + RESPONSE• 1) Detection issues – correction factors for fast

water habitats• 2) habitat characteristics - what are the most

important physical variables in tidal sites (and wave sites?) .

• 3) What level of detail is needed to define – visual, acoustics,Mike21, radar?

• 4) Risk of collision by understanding / quantified foraging behaviours in tidal (and wave sites?) – fish, birds, mammals?

• 5) Large/far scale potential cumulative effects on populations of mobile predators

Artic Tern Surface feeder

(mainly ebb tides and in habitat with boils & shear ) 10

20

30

40

1 2 3 4 5 6

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Tidalspeed

Habitat

Hab

itat

TIDAL SPEED

1.0

1.2

1.4

1.6

1.8

1 2 3 4 5 6

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Tid

Hab

TIDAL SPEED

Hab

itat

Black Guillemot Diving bird

(Across wider range of tidal states and within

relatively more laminar flow)

Mean number of actively foraging birds

IE MaxE DE IF MaxF DF