Bell, PS

Cazenave, P

Scott, BE

Torres, R

Waggitt, JJ

Williamson, BJ

Plymouth Marine Science Electronic Archive (PlyMSEA)

J.J. Waggitt 1: P.S. Bell 2: P. Cazenave 3: R. Torres 3: B.J Williamson1, 4: B.E. Scott1    1 Institute of Biological and Environmental Sciences, Zoology Building, University of Aberdeen, Aberdeen AB24 2TZ, UK 2 National Oceanography Centre, Brownlow Street, Liverpool L3 5DA, UK 3 Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK 4 Department of Physics, University of Bath, Claverton Down, Bath BA2 7AY, UK A combination of empirical and modelled datasets reveals associations between deep diving seabirds and oceanographical processes at fine spatiotemporal scales in a high energy habitat   •Impacts of tidal stream turbines on seabird populations unknown.   •Quantifying collision risks between deep diving seabirds and devices prioritised.    •Estimating spatial overlap an essential component of quantifying collision risk   •Understand and predict seabird foraging distributions within the tidal pass habitats favoured for installations   Collision Risks and Spatial Overlap Fall Of Warness, Orkney, UK Ebb Flood •Typical tidal pass habitat •Strong bidirectional currents •Complex topography •Complex bathymetry Seabed Roughness (Arbitrary Unit) Seabed Hardness (Arbitrary Unit) Depth  (Metres)  Slope  (Degrees) Bathymetry is from multibeam sonar Seabed characteristics are from echosounder Fall Of Warness, Orkney, UK •Ebb-Flood Tidal Cycle •Variations in feature location •Upwelling/Downwelling •Turbulence •Current Speeds Ebb   Flood  Upwelling  ( cm/s)  Turbulence  (Eddy Viscosity) Speed (m/s) FVCOM 3D Hydrodynamic Model Outputs Fall Of Warness, Orkney, UK •Neap-Spring Tidal Cycle •Variations in feature extent •Upwelling/Downwelling •Turbulence •Current Speeds FVCOM 3D Hydrodynamic Model Outputs Neap (Ebb)  Spring (Ebb) Upwelling  ( cm/s)  Turbulence  (Eddy Viscosity) Speed (m/s) Understanding and Predicting Seabird Distributions •Seabird associate with physical conditions that promote prey availability.   •Prey availability difficult to collect and quantify over entire tidal pass habitats.    •Understanding associations between seabirds and physical conditions enables predictions of distributions.    •Concurrent seabird and physical datasets need collecting over several seasons and tidal states.   Suitable Physical Conditions Increased Prey Availability Presence of Foraging Seabirds Predictions of foraging Distributions Vessel Based Observers  103 transects  May and October  2012 and 2013  Zigzag route against currents   Only seabirds upon the sea surface recorded  Positions calculated to an estimated accuracy of several hundred metres      Seabird Distributions Seabird Distributions Atlantic Puffin Fratercula arctica Feed primarily within water column Present during May   4 Abundant Deep Diving Species Behavioural Differences Ecological Differences Seabird Distributions Common Guillemot Uria aalge  Feed primarily within water column Present during May   4 Abundant Deep Diving Species Behavioural Differences Ecological Differences Seabird Distributions Black Guillemot Cepphus grylle  Feed primarily upon seabed Present during May and October.  4 Abundant Deep Diving Species Behavioural Differences Ecological Differences Seabird Distributions European Shag Phalacrocorax aristotelis Feed primarily upon seabed  Present during May and October. 4 Abundant Deep Diving Species Behavioural Differences Ecological Differences Seabird Abundances Atlantic Puffin (May) Black Guillemot (May, Oct) Common Guillemot (May) European Shag (May, Oct) General Linear Mixed Effect Models Environmental Variables Speed Turbulence Upward Currents Seabed (Roughness/Hardness)   Run Model Poisson Distribution Selected Models using p-values Random Variables DateTime   Predictions Used Model Coefficients  Seabird Foraging Distribution Dataset Physical  Conditions Dataset Season Species Speed Turbulence Upwelling Substrate Summer Atlantic Puffin Positive Positive      Summer Common Guillemot Positive       Summer Black Guillemot Negative Positive Downwelling  Soft/Rough Winter Black Guillemot Positive   Neither  Soft/Rough Summer European Shag     Downwelling  Soft/Rough Winter European Shag Negative      Soft/Rough Model Outputs •Variations in microhabitat associations among species.  •Seasonal variations in species microhabitat associations.  •Variations in microhabitat associations complexities.   •Benthic foragers always associated with soft/rough substrate.  •Pelagic foragers always associated with fast current speeds Predicted Distributions  Ebb  Flood Atlantic Puffin (Summer) Neap Spring High Spatial Overlap Increases during spring tides Increases during ebb tides Black Lines = Turbine Area Predicted Distributions  Common Guillemot (Summer) Ebb  Flood Neap Spring High Spatial Overlap Increases during spring tides Increases during ebb tides Black Lines = Turbine Area Predicted Distributions  Black Guillemot (Summer) Ebb  Flood Neap Spring Low Spatial Overlap   Black Lines = Turbine Area Predicted Distributions  Black Guillemot (Winter) Ebb  Flood Neap Spring Moderate Spatial Overlap   Black Lines = Turbine Area Predicted Distributions  European Shag (Summer) Ebb  Flood Neap Spring Low Spatial Overlap    Black Lines = Turbine Area Predicted Distributions  European Shag (Winter) Ebb  Flood Neap Spring Black Lines = Turbine Area Low Spatial Overlap    Conclusions Differences in associations among species and within species over time.   Several ecological explanations relating to resource competition and foraging behaviours   However, results highlight which and when species are most likely to forage near tidal stream turbines.   Quantitative measures enable predictions of spatial overlap at population levels  Atlantic Puffin Summer Resident High Spatial Overlap  Increases in Ebb and Spring Tides Black Guillemot Summer and Winter Resident Moderate Spatial Overlap in Winter European Shag Summer and Winter Resident Low Spatial Overlap  Common Guillemot Summer Resident High Spatial Overlap  Increases in Ebb and Spring Tides Acknowledgements Marine Scotland Eric Armstrong Ian Davies Mike Stewart Robert Watret   Seabird Observations Marianna Chimienti Ciaran Cronin Stuart Thomas Tim Sykes  Bathymetric Data Russell Wynn  Discussions Paul Fernandes Kirsty Lees Alex Robbins Jane Reid Helen Wade  Statistical Advice Thomas Cornulier Alex Douglas James Grecian Samantha Patrick  EMEC Cristina Bristow Matthew Finn Jennifer Norris  Openhydro Sue Barr  Eday Community Jenny Campbell Adam Cockram Kate Cockram Louise Cockram Mark Cockram Mike Cockram      

A combination of empirical and modelled datasets reveals associations between deep diving seabirds and oceanographical processes at fine spatiotemporal scales in a high energy habitat

A combination of empirical and modelled datasets reveals associations between deep diving seabirds and oceanographical processes at fine spatiotemporal scales in a high energy habitat

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