1. Identifying marine SPAs for larger tern species breeding in the UK Wilson LJ, Black J, Brewer MJ, Potts JM, Kuepfer A, Win I, Kober K, Bingham C, Mavor R & Webb A

2. = 32 SPAs (46 species/colonies) Prioritising ‘recently occupied’ SPA colonies Sandwich tern Background Roseate tern Arctic tern Common tern 41 SPAs for larger terns (62 species/SPAs) Identifying additional marine areas to colony SPAs

3. Habitat modelling Targeted survey at selected colonies Model tern data with predictor variables (e.g. depth) Methods: approach Two phases: 1.Site-specific models (‘data rich’ sites) 2.Generic models (‘data poor’ sites)

4. Methods: data collection Three years data collection JNCC 2009-2011 Additional years data under data sharing agreement 2006-2009 Visual tracking method Environmental information collated

5. Data coverage Data from 11 colony SPAs 1-3 years per colony 1275 tracks SpeciesN. tracksNo. SPAs Arctic1856 (+1 non SPA) Common4078 (+1 non SPA) Sandwich6286 (+ 1 non SPA) Roseate552 Data from 3 colony SPAs in Scotland 265 tracks in Scotland (20%)

6. Max potential foraging range Methods: analysis Compare environment of foraging locations with unused locations C Use model to predict usage across entire foraging range Regression model predicts probability of a location being used for foraging Identify habitat preference relationships using regression models (GLM) covariate s(covariate)

7. Phase 1 and 2 models Site specific model Usage Colony A + Colony B Colony C Usage Generic model Colony A

8. PHASE 1 example: distance to colony, slope, sea surface salinity in spring, distance to shore, bathymetry Data from 2 years, 137 tracks

9. PHASE 2 example: Swona, Arctic tern Data from 4 SPAs, 147 tracks, 10 site/years. Model covariates: Distance to colony Depth Arctic tern

10. Model performance: Cross validation Training set Test set Build model How well does model predict test dataset? Sub-setting can be done by: o Individual or Year (phase 1) o Colony (phase 2)

11. Based on the law of diminishing returns Looks at relationship between cumulative area and cumulative usage Identifies the point beyond which disproportionately large areas are required to support the same number of birds Boundary Delineation. Maximum Curvature. OBJECTIVE method of identifying a density or usage threshold.

12. Maximum Curvature Cumulative usage Density at this point is the density threshold for inclusion within boundary Usage at this point is the usage threshold for inclusion within a boundary

13. Applying the threshold Common terns, Imperial Dock Lock SPA Maximum curvature threshold: usage ≥ 0.419

14. Summary Data collection –3 years, geographic spread –Visual tracking Model outputs –Habitat suitability (preference) –Usage Boundary analysis –Maximum curvature

15. Identifying important marine areas for the little tern Sternula albifrons BOAT surveys to identify seaward extent. Shore based counts to identify along-shore extent. 3 years of shore based counts: site specific along shore extent. 1 year of boat surveys: insufficient data for site specific seaward extent, use generic seaward extent instead.

16. Identifying important marine areas red throated diver breeding in Scotland Julie Black, Ben Dean and Seabirds & Cetaceans Team Aberdeen Photo © Jakob Sigurðsson

17. Background Identifying additional marine areas to colony SPAs 10 colony (breeding territory) SPAs Breeds on lochs and lochans, forages close inshore Data collection around most important breeding areas and providing geographic spread Modelling approach allowing identification of important marine habitat around all colony SPAs

18. Sue O’BrienSue O’Brien Boat based transect surveys 2003 – 2007 to sample foraging locations Survey methods Kerstin Kober Radio tracking to estimate foraging range

19. Prediction approach Foraging location data Shetland Environmental variables Shetland, Orkney and North Uist Foraging habitat preferences Scotland marine inshore environment + Foraging location data Orkney + Environmental variables Scotland marine inshore environment + Foraging location data North Uist + Regression model (GAM)

20. Predictions for Scotland, up to 10km from coast Predicted habitat suitability OrkneyShetland Western Isles Rum

21. Maximum Curvature Density at this point is the density threshold for inclusion within boundary

23. RANKING Average habitat suitability Number nests within foraging range Number breeding territory SPAs within foraging range Regularity of use ( BTO 1988-1991 atlas ) Confidence: expert opinion exercise Site Prioritisation

24. Summary Data collection –5 years, geographic spread –Boat transects and radio tracking Model outputs –Habitat suitability –Suitability combined with potential usage (nests within foraging range) Boundary analysis –Maximum curvature Site prioritisation –Ranking exercise

26. Identifying important marine areas for shags in the UK Julie Black, Matt Parsons and Jim Reid

27. Background 13 colony (breeding territory) SPAs Forages close inshore AIM –UK suite of shag sites –Summer and winter –Capturing highest densities –Based on data availability

29. Inshore aerial surveys –North Orkney –Scapa Flow ESAS data base –East Caithness Cliffs Tracking data –Isle of May –Isles of Scilly –Farnes Island Available data

30. Isle of May Part of Firth of Forth Islands SPA 322 shags tracked between 1987 and 2010 Kernel density analysis of year by year data Maximum curvature of kernels of all years combined –93% Kernel Density Contour