4.6. Arctic tern

  1. Monthly estimates of collisions for the worst-case for Arctic tern are presented in Table 4.19   Open ▸ for both the Developer and Scoping Approaches.
  2. The estimated number of collisions was highest during August, when  densities of flying Artic terns were at their highest, with 1.57 birds/km2 (pooled SD ±0.35) and 2.60 birds/km2 (pooled SD ±0.33) used in the Developer and Scoping Approaches respectively ( Table 3.3   Open ▸ and Table 3.4   Open ▸ ).
  3. Combining the estimated mortality across bio-seasons, shows that the estimated number of collisions is highest during the breeding season Table 4.20   Open ▸ .
  4. The estimated number of collisions presented in Table 4.19   Open ▸ and Table 4.20   Open ▸ were used in population modelling reported in Technical Appendix 11.6: Ornithology Population Viability Analysis.
  5. Annual collision estimates for Arctic terns for all turbine scenarios and avoidances rates using the Developer and Scoping Approaches are presented in Table 4.21   Open ▸ and Table 4.22   Open ▸ respectively.

 

Table 4.19:
Monthly estimated collisions for Arctic tern in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980).

Table 4.19:  Monthly estimated collisions for Arctic tern in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980).

 

Table 4.20:
Estimated number of collisions for Arctic tern by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).

Table 4.20:  Estimated number of collisions for Arctic tern by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).

*Collisions during the breeding and non-breeding seasons equate to 13.832 and 0.133 respectively (both rounded down to the nearest two decimal places numbers) and the total annual collisions equate to 13.965, rounded up to the nearest two decimal places number.


Table 4.21:
Summary of estimated number of annual collisions for Arctic tern from the Band model using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

Table 4.21:  Summary of estimated number of annual collisions for Arctic tern from the Band model using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

*The estimated collisions for 14MW and 18MW turbines type A are 8.10 and 7.97 per annum, respectively.

 

Table 4.22:
Summary of estimated number of annual collisions for Arctic tern from the Band model using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates are rounded to the nearest whole.

Table 4.22:  Summary of estimated number of annual collisions for Arctic tern from the Band model using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates are rounded to the nearest whole.

*The estimated collisions for 14MW and 18MW turbines type A are 13.97 and 13.75 per annum, respectively.


4.7. Common tern

  1. Monthly estimates of collisions for the worst-case for common tern are presented in Table 4.23   Open ▸ for both the Developer and Scoping Approaches.
  2. The estimated number of collisions was highest during August, when the mean and max monthly densities of flying common terns were at their highest, with 0.45 birds/km2 (pooled SD ±0.13) and 0.69 birds/km2 (pooled SD ±0.18) respectively ( Table 3.3   Open ▸ and Table 3.4   Open ▸ ).
  3. Combining the estimated monthly mortality across bio-seasons shows that the estimated number of collisions is highest during the breeding season ( Table 4.24   Open ▸ ).
  4. The estimated number of collisions presented in Table 4.23   Open ▸ and Table 4.24   Open ▸ were used in population modelling reported in Technical Appendix 11.6: Ornithology Population Viability Analysis.
  5. Annual collision estimates for common terns for all turbine scenarios and avoidance rates using the Developer and Scoping Approaches are presented in Table 4.25   Open ▸ and Table 4.26   Open ▸ respectively.

 

Table 4.23:
Monthly estimated collisions for common tern in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980).

Table 4.23:  Monthly estimated collisions for common tern in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980). 

 

Table 4.24:
Estimated number of collisions for common tern by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).

Table 4.24:  Estimated number of collisions for common tern by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).


Table 4.25:
Summary of estimated number of annual collisions for common tern from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

Table 4.25:  Summary of estimated number of annual collisions for common tern from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

*The estimated collisions for 14MW and 18MW turbines type A are 6.05 and 5.89 per annum, respectively.

 

Table 4.26:
Summary of estimated number of annual collisions for common tern from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates are rounded to the nearest whole.

Table 4.26:  Summary of estimated number of annual collisions for common tern from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates are rounded to the nearest whole.

*The estimated collisions for 14MW and 18MW turbines type A are 9.15 and 8.90 per annum, respectively.


4.8. Little gull

  1. Monthly estimates of collisions for the worst-case for little gull are presented in Table 4.27   Open ▸ for both the Developer and the Scoping Approaches.
  2. The estimated number of collisions was highest during August, when the densities of flying little gulls were at their highest, with 0.07 birds/km2 (pooled SD ± 0.04) and 0.12 birds/km2 (pooled SD ±0.05) respectively ( Table 3.3   Open ▸ and Table 3.4   Open ▸ ).
  3. Combining the estimated mortality across bio-seasons, shows that the estimated number of collisions is highest during the non-breeding season. This is mainly explained as little gulls do not breed in the UK and their presence and, therefore collision risk, is mainly confined to the non-breeding season ( Table 4.28   Open ▸ ).
  4. The estimated number of collisions presented in Table 4.27   Open ▸ and Table 4.28   Open ▸ were used in population modelling reported in Technical Appendix 11.6: Ornithology Population Viability Analysis.
  5. Annual collision estimates for little gulls for all turbine scenarios and avoidance rates using the Developer and Scoping Approaches are presented in Table 4.29   Open ▸ and Table 4.30   Open ▸ respectively.

 

Table 4.27:
Monthly estimated collisions for little gull in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980).

Table 4.27:  Monthly estimated collisions for little gull in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2), based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.980).

 

Table 4.28:
Estimated number of collisions for little gull by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).

Table 4.28:  Estimated number of collisions for little gull by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 2).


Table 4.29:
Summary of estimated number of annual collisions for little gull for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

Table 4.29:  Summary of estimated number of annual collisions for little gull for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

*The estimated collisions for 14 MW; 15 MW; 18 MW; 21 MW and 24 MW turbines type A are 2.45; 2.04; 2.38; 2.19 and 2.05 per annum, respectively.

**The estimated collisions for 18 MW; 21 MW and 24 MW turbines type B are 1.98; 1.86 and 1.77 per annum, respectively.

 

Table 4.30:
Summary of estimated number of annual collisions for little gull for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

Table 4.30:  Summary of estimated number of annual collisions for little gull for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014). Estimates equal or greater than 0.5 are rounded to the nearest whole.

*The estimated collisions for 18MW; 21MW and 24MW turbines type B are 1.98; 1.86 and 1.77 per annum, respectively.

 

4.9. Great skua

  1. Collisions of great skua estimated by the Band method were close to zero in all of the scenarios for both the Developer and Scoping Approaches, therefore, further breakdowns beyond those given in Table 4.31   Open ▸ and Table 4.32   Open ▸ are not presented.
Table 4.31:
Summary of estimated number of annual collisions for great skua for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014).

Table 4.31:  Summary of estimated number of annual collisions for great skua for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Developer Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014).

 

Table 4.32:
Summary of estimated number of annual collisions for great skua for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014).

Table 4.32:  Summary of estimated number of annual collisions for great skua for the five wind turbine generator sizes from the Band model Options 2 and 3 using the Scoping Approach and generic flight height data, for turbine Type A (wide chord and slow rotational speed) and B (narrow chord and fast rotational speed). Avoidance rates are from SNCBs (2014).


5. Conclusion

  1. For this report, the worst-case estimated number of annual collisions for each species was identified from the outputs of the deterministic Band (2012) model. The scenarios differed in the rating and type of turbine (based on chord width and rotational speed) and model Option chosen. However, we limited our search for the worst-case to scenarios that used generic flight height data of Johnston et al. (2014a; 2014b) only (i.e. Options 2 and 3) as this has been endorsed by SNCBs (SNCBs, 2014) and advised in the Scoping Opinion (4 February 2022); this represents the Scoping Approach for assessing collision risk for seabird species at the Proposed Development Array.
  2. The results show that the worst-case ornithology collision impacts for all species modelled are predicted for the 14MW turbine and hence it is these values that are taken forward into the Population Viability Analysis (Technical Appendix 11.6) and assessed in the Environmental Statement.
  3. The worst-case turbine scenario was the same (14 MW) for all species regardless of which avoidance rates or Approach was used. However, for kittiwake and gannet, estimated collisions were considerably lower when using Bowgen & Cook (2018), with a reduction of 9% and 54% respectively, when compared with outputs using SNCBs (2014) avoidance rates for both the Developer and Scoping Approaches.
  4. For comparison of the worst-case scenario from the Band (2012) model, the sCRM was also used. Using the Developer Approach (which differs from the Scoping Approach by use of mean monthly densities, rather than monthly maximum densities), the results from the sCRM for kittiwake were considerably lower (-46%). For other species, sCRM estimates were also lower for lesser black-backed gull (-33%) and herring gulls (-58%) unchanged for common tern, and higher for Arctic tern (+43%), little gull (-80%) and great skua (+83%). Similar results were obtained when using the Scoping Approach. The results from the sCRM were lower for kittiwake, herring gull and lesser black-backed gull (-46%, -36%, -33% respectively). For other species, sCRM estimates were unchanged for common tern, and higher for Arctic tern (+36%), little gull (-64%) and great skua (+65%).
  5. Due to its stochastic nature, estimates from the sCRM are not directly comparable with Band outputs because the output is a distribution rather than a single estimate of collisions. Recommended avoidance rates also differ between Band and sCRM methods.
  6. The estimated annual number of collisions was greatest for kittiwake at 685 birds per annum using the Developer Approach and 986 using the Scoping Approach based on Band model Option 2 and using generic flight height data and SNCBs (214) avoidance rates. Use of site-specific boat-based flight height data resulted in significantly lower annual mortality estimates: based on rangefinder data, the mean estimated annual number of collisions for kittiwake using the Developer and Scoping Approaches were 56 and 81 birds respectively, and visual observer data, the annual mean was 225 and 324 kittiwakes for the Developer and Scoping Approaches respectively.

6. Summary

  1. Berwick Bank Wind Farm Limited (BBWFL) (the ‘Applicant’) is a wholly owned subsidiary of SSE Renewables Limited and is developing the Berwick Bank Wind Farm (the ‘Project’) located at the mouth of the North Sea’s Firth of Forth.
  2. Digital aerial surveys were flown March 2019 – April 2021 to provide two years of baseline data collection on the seabirds and other marine megafauna in the Offshore Ornithology Study Area (Array area and a 16km buffer). Monthly estimates of flying birds within the proposed Array area only were used in the CRM reported here.
  3. Collision risk estimates are presented for eight species vulnerable to collision at OWFs: kittiwake Rissa tridactyla, herring gull Larus argentatus, lesser black-backed gull Larus fuscus, gannet Morus bassanus, Arctic tern Sterna paradisaea, common tern Sterna hirundo, little gull Hydrocoloeus minutus and great skua Stercorarius skua.
  4. Two approaches to CRM have been used:
  • Deterministic offshore Band CRM (Band, 2012); and
  • sCRM (Masden, 2015; McGregor et al., 2018).
  1. The results from the Band CRM will be those relied upon for further analyses (i.e. PVA) given that this Approach utilises endorsed SNCBs avoidance rates. The sCRM approach, which takes account of the uncertainty around input parameters, is used only for comparative purposes of the worst-case.
  2. Only Band models 2 (basic) and 3 (extended) were used as they use the generic flight height data of Johnston et al. (2014a; 2014b). The models were parameterised using SNCBs (2014) advised avoidance rates; Bowgen and Cook (2018) avoidance rates; seabird morphometric data from Pennycuick (1997) and Alerstam et al. (2007); nocturnal activity from Garthe and Hüppop (2004), Furness et al. (2018) and the Seagreen EIA Optimised Project Addendum (2018); and flight speed from Pennycuick (1997).
  3. The Band models were run for each of the five turbine scenarios. Additionally, scenarios for turbines 18 MW – 24 MW were of two types: Type A (wide chord and slower rotational speed) and Type B (narrower chord and faster rotational speed).
  4. Estimates of collisions were calculated for each bio-season based on definitions based on NatureScot, (2020) for the breeding and non-breeding seasons. Non-breeding seasons for kittiwake and gannet were based on the spring and autumn migration periods defined by the BDMPS of Furness (2015).
  5. Following the release of the Scoping Opinion and associated Consultee representations and advice, the Applicant determined to undertake a “dual assessment” of the collision risk posed by the Project:
  • Scoping Approach: monthly maximum density of relevant seabird species within the Development Array area are to be used in the CRMs based on NatureScot and MSS recommendation; and
  • Developer Approach: monthly mean density of relevant seabird species within the Development Array area are to be used in the CRMs based on the Applicant opinion.
  1. The 14 MW turbine size resulted in the worst-case ornithology collision impacts across all species and hence it is these values that are taken forward into the PVA and assessed in the Environmental Statement. The estimated number of collisions per annum using the deterministic Band model was highest for kittiwake (685 birds for the Developer Approach and 986 birds for the Scoping Approach). Estimates were shown to be sensitive to the source of flight height data used in the Band model. Based on site-specific rangefinder data, the mean estimated annual number of collisions for kittiwake using the Developer and Scoping Approaches were 56 and 81 birds respectively. Using the visual observer collected site- data, the annual mean was 225 and 324 kittiwakes for the Developer and Scoping Approaches respectively (
    Table 3 Estimated number of collisions for kittiwake by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1) using boat-based data    ).    
  2. The annual estimate of gannet collisions was 153 birds for the Developer Approach and 191 birds for Scoping Approach.  
  3. For herring gull, the worst-case estimate was 30 birds per annum based on the Developer Approach and 50 for the Scoping Approach; for all other species, annual estimated collisions based on the Developer Approach were of eight birds or less, compared with 14 birds or less using the Scoping Approach. Near zero collisions for great skua were predicted using both Approaches. 
  1. The view that using fewer, larger turbines as an effective measure for reducing collision (Johnston et al., 2014a; 2014b) was borne out by the modelling using Band Option 2 undertaken here. For all species, the number of collisions tended to decrease with increasing turbine size and (amongst the three larger turbine models which had different chord width and rotor speed Options) was lower for Type B turbines (narrower chord and faster rotational speed).  
  2. The embedded mitigation in the turbine design, that increases the air gap from 22 m to 37 m (LAT) results in a reduction in the estimated annual number of collisions of ~76% and 79% for kittiwake and gannet respectively.    

7. References

Alerstam, T., Rosén, M., Bäckman, J., Ericson, P.G. and Jellgren, O. (2007). Flight speeds among bird species: allometric and phylogenic effects. PLoS Biology, 5(8), e197.

 

Band, W. (2012). Using a collision risk model to assess bird collision risks for offshore wind farms – with extended method. Report to the Crown Estate Strategic Ornithological Services (SOSS).

 

Bowgen, K. and Cook, A. (2018). Bird Collision Avoidance: Empirical evidence and impact assessments. JNCC Report No. 614, JNCC, Peterborough, ISSN 0963-8091.

 

Camphuysen, K.C.J., Fox, T.A.D., Leopold, M.M.F. and Petersen, I.K. (2004). Towards standardised seabirds at sea census techniques in connection with environmental impact assessments for offshore wind farms in the U.K. - A comparison of ship and aerial sampling methods for marine birds, and their applicability to offshore wind farm assessments. Report COWRIE - BAM -02-2002.

 

Cook, A.S.C.P., Johnston, A., Wright, L.J. and Burton, N.H.K. (2012). Strategic Ornithological Support Services. Project SOSS-02. A review of flight heights and avoidance rates of birds in relation to offshore wind farms. BTO Research Report 618.

 

Cook, A.S.C.P., Humphreys, E. M., Masden, E. A. and Burton, N. K. H. (2014). The avoidance rates of collision between birds and offshore turbines. Scottish Marine and Freshwater Science Volume 5, Number 16. 263pp.

 

Cook, A.S.C.P. (2021). Additional analysis to inform SNCBs recommendations regarding collision risk modelling. BTO Research Report 739.

 

Furness, R.W., Wade, H.M. and Masdfen, E.A. (2013). Assessing vulnerability of marine bird populations to offshore wind farms. Journal of Environmental Management, 119, 56-66. 

 

Furness, R.W., Garthe, S., Trinder, M., Matthiopoulos, J., Wanless, S. and Jeglinski, J. (2018). Nocturnal flight activity of northern gannets Morus bassanus and implications for modelling collision risk at offshore wind farms. Environmental Impact Assessment Review, 73, 1-6.

 

Garthe, S. and Hüppop, O. (2004) Scaling possible adverse effects of marine wind farms on seabirds: developing and applying a vulnerability index. Journal of Applied Ecology, 41(4), 724-734.

 

Johnston, A., Cook, A.S., Wright, L.J., Humphreys, E.M. and Burton, N.H. (2014a). Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines. Journal of Applied Ecology, 51(1), 31-41.

 

Johnston, A., Cook, A.S., Wright, L.J., Humphreys, E.M. and Burton, N.H. (2014b). Corrigendum to Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines. Journal of Applied Ecology, 51: 1126 -1130.

 

Jongbloed, R.H. (2016) Flight height of seabirds. A literature study IMARES. Report C024/16.

 

Marine Scotland. (2017a) Scoping Opinion for the proposed Section 36 Consent and Associated Marine Licence Application for the Revised NnG Offshore Wind Farm and Revised NnG Offshore Transmission Works. Available from http://www.gov.scot/Topics/marine/Licensing/marine/scoping/NnGRev2017/SO-092017

 

Marine Scotland. (2017b). Scoping Opinion for the proposed Section 36 Consent and Associated Marine Licence Application for the revised Inch Cape offshore wind farm and revised Inch Cape Offshore Transmission Works.

 

Masden, E. (2015). Developing an Avian Collision Risk Mode to Incorporate Variability and Uncertainty. Scottish Marine and Freshwater Science Vol 6 No 14. Edinburgh: Scottish Government, 43pp.

 

McGregor, R.M., King, S., Donovan, C.R., Caneco, B. and Webb, A. (2018). A Stochastic Collision Risk Model for Seabirds in Flight. Report for Marine Scotland.

 

NatureScot. (2020). Seasonal periods for birds in the Scottish marine environment. Short Guidance Note Version 2. NatureScot.

 

Parker, J., Fawcett, A., Banks, A., Rowson, T., Allen, S., Rowell, H., Harwood, A., Ludgate, C., Humphrey, O., Axelsson, M., Baker, A. and Copley, V. (2022). Offshore Wind Marine Environmental Assessments: Best Practice Advice for Evidence and Data Standards. Phase III: Expectations for data analysis and presentation at examination for offshore wind applications. Natural England. Version 1.2. 140 pp.

 

Pennycuick, C.J. (1997). Actual and ‘Optimum’ Flight speed: Field Data Reassessed. Journal of Experimental Biology, 200(17), 2355-2361.

 

Robinson, R.A. (2005) BirdFacts: profiles of birds occurring in Britain & Ireland (BTO Research Report 407). BTO, Thetford. http://www.bto.org/birdfacts. [Online]. Accessed 13/01/2022.

 

Seagreen EIA Optimised Project Addendum (2018). Optimisation application.  https://www.seagreenwindenergy.com/optimised-application-2018-document. [Online]. Accessed 13/01/2022.

Skov, H., Heinänen, S., Norman, T., Ward, R.M., Méndez-Roldán, S. and Ellis, I. (2018). ORJIP Bird Collision and Avoidance Study. Final report. The Carbon Trust. United Kingdom.

 

SNCBs. (2014). Joint Response from the Statutory Nature Conservation Bodies to the Marine Scotland Science Avoidance Rate Review. SNCBs.

Annex A. To The Ornithology Collision Risk Modeling Technical Report: Worst Case Scenario 22 m Air Gap Collision Estimates

  1. The Applicant has chosen wind turbine generators with mitigation embedded in the design. The minimum lower blade tip height has been increased from 22 m to 37 m (LAT) as an engineering design measure to increase the lower air gap. The larger air gap was predicted to have a positive impact on reducing the number of seabirds at risk of collision with the turbines. The Scoping Opinion requires that any embedded mitigation relied upon for the purposes of the assessment should be clearly and accurately explained in detail within the EIA Report.
  1. Therefore, Collision Risk Models (CRMs) were run using the deterministic Band on the worst-case scenario (SNCBs (2014) avoidance rates, turbine 14 MW, Type A) for both kittiwake ( Table 1   Open ▸ ) and gannet ( Table 2   Open ▸ ), for both the Developer and Scoping Approaches, but the hub height relative to LAT, reduced from 148 m to 133 m for the results presented in this Annex. All other input parameters were exactly as set out in Section 3.4.
  2. The estimated number of collisions were considerably higher with a 22 m airgap compared with the larger 37 m. By using a larger airgap, the estimated number of collisions was ~76% and 79% lower for kittiwake and gannet respectively.
Table 1 :
Comparison of the estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Type A) with a 22 m and 37 m air gap, based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates are rounded to the nearest whole

Annex A Table 1 Comparison of the estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Type A) with a 22 m and 37 m air gap, based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates are rounded to the nearest whole


Table 2 :
Comparison of the estimated annual collisions for gannet in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Type A) with a 22 m and 37 m air gap, based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates are rounded to the nearest whole

Annex A Table 2 Comparison of the estimated annual collisions for gannet in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Type A) with a 22 m and 37 m air gap, based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates are rounded to the nearest whole


Annex B. To The Ornithology Collision Risk Modelling Technical Report: Boat-Based Kittiwake Collision Estimates

 

  1. Boat based surveys were undertaken from dawn until dusk on 2 – 6 July and 6 – 9 August 2020, which equated to nine days of boat-based surveys in total. ECON Ecological Consultancy Ltd and RPS surveyed short transects within the survey area and following the boat-based European Seabirds At Sea methods (ESAS, Camphuysen et al., 2004) with a 300 metre transect width.
  2. ESAS methods require that observers scan ahead of the boat, using binoculars on occasion, within a 90-degree arc and within a 300 m strip on one side of the ship. While a flight height will be estimated for each bird recorded, including for those flying ahead, the principal focus for the search pattern is ahead of the boat (Camphuysen et al., 2004). Flying birds were estimated to be in 5m flight height bands (i.e. >0-5, >5-10, >10-15) using methods presented in (Camphuysen et al., 2004). Three observers were present on board the boat; two to complete the visual surveys and one to use the rangefinder. Visual and rangefinder surveys took place on opposite sides of the observation deck.
  3. The RPS boat-based visual and rangefinder surveys were conducted in tandem but using different observers on opposite sides of the observation platform. Visual estimates of flight height were recorded for every bird encountered. Estimates of flight height using the rangefinder aimed to detect every bird encountered, but during busy periods collision risk modelling target species or data-poor species were preferentially recorded.
  4. The two survey methods yield different estimates of the proportion of birds at collision risk height (PCH) for both months for a wind turbine scenario of 32 – 252 m PCH relative to Mean High Water Spring (MHWS). The visual method estimates average PCH to be between 0.8% and 2.32% for kittiwakes. The rangefinder method estimates average PCH to be 0% for kittiwakes in both months (Appendix 11.7: Comparison of boat-based and digital video aerial survey methods for seabirds).
  1. Using the worst-case scenario (14 MW, Type A, SNCBs avoidance rates), monthly estimates of annual collisions for kittiwake were generated using the basic Band model and Option 1, to incorporate site specific flight height information from the boat-based rangefinder ( Table 1   Open ▸ Table 3   Open ▸ ) and visual observer ( Table 2   Open ▸ data; these are presented for both the Developer and Scoping Approaches. Estimated monthly number of collisions were also combined across bio-seasons and are presented in Table 3   Open ▸ .
  2. Compared to estimated annual number of collisions using the generic flight height data for kittiwake ( Table 4.3   Open ▸ : 685 for the Developer Approach and 986 for the Scoping Approach), the results from using site-specific flight heights from rangefinder and visual observer data were considerably lower ( Table 2   Open ▸ and Table 3   Open ▸ ).
  3. Based on rangefinder data, the mean estimated annual number of collisions for kittiwake using Developer and Scoping Approaches were 56 and 81 birds respectively. Using the visual observer collected data, the annual mean increased to 225 and 324 kittiwakes for the Developer and Scoping Approaches respectively ( Table 3   Open ▸ ).    

 

Table 1 :
Monthly estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1), using boat-based data collected with a rangefinder and based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates equal or greater than 0.5 are rounded to the nearest whole

Annex B Table 1 Monthly estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1), using boat-based data collected with a rangefinder and based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates equal or greater than 0.5 are rounded to the nearest whole

Table 2 :
Monthly estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1), using boat-based data collected visually and based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates equal or greater than 0.5 are rounded to the nearest whole

Annex B Table 2 Monthly estimated annual collisions for kittiwake in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1), using boat-based data collected visually and based on the Developer and Scoping Approaches. Estimates are presented using the mean avoidance rate (0.989) and for the mean avoidance rate ±2 SD (0.002). Estimates equal or greater than 0.5 are rounded to the nearest whole


Table 3 :
Estimated number of collisions for kittiwake by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1) using boat-based data

Annex B Table 3 Estimated number of collisions for kittiwake by season in the Proposed Development Array for the worst-case scenario (SNCBs avoidance rates, turbine 14 MW, Option 1) using boat-based data

 

Table 4 :
Proportion of birds at collision height estimated from the site-specific survey data (Option 1 sCRM)

Annex B Table 4 Proportion of birds at collision height estimated from the site-specific survey data (Option 1 sCRM)