Offshore wind energy - sectoral marine plan: seabird tagging feasibility

How to undertake a seabird tagging study for species and colonies potentially impacted by the sectoral marine plan for offshore wind energy

Link to Roadmap of Actions and Marine Sectoral Plan

The Roadmap of Actions[3] to support the Marine Sectoral Plan highlighted four key outcomes that were required in order to assess whether or not the risk to seabird populations within Plan Options E3, NE2, NE3, NE4 and NE6 could be reduced to an acceptable level:

1. Reduced uncertainty over connectivity between plan options and designated features of SPAs in the breeding and non-breeding seasons

2. Reduced uncertainty over collision, displacement, and barrier effects in each plan option

3. Understanding of population-level impacts on the populations concerned

4. Understanding of the contribution of marine spatial planning and mitigation to reducing impacts and unlocking plan option potential for Offshore Wind Farms.

Seabird tracking studies, whether using GPS or geolocation tags, have the potential to feed into each of these. At a most basic level, GPS tracking would make it possible to establish connectivity between the Plan Options and SPAs during the breeding season, whilst geolocators would allow similar inferences to be made during the non-breeding season (subject to the greater degree of uncertainty surrounding fixes from these devices) (OUTCOME 1). Geolocation tag data would also feed into the need to address uncertainties relating to seabird distributions within Plan Options E1 and E2 during the non-breeding season.

Data additional to location can be obtained from GPS tags combined with other sensors, notably information on flight heights and speeds. Similarly, by deploying time-depth recorders alongside geolocators or GPS tags, information on diving behaviour can be derived. As a first step, these data can be used to inform estimates of flight height and speeds used in collision risk models (Largey et al., 2021; Masden & Cook, 2016; Masden et al., 2021; Ross-Smith et al., 2016), reducing uncertainty in the outputs from these models. (OUTCOME 2). These data can also be used to inform behavioural classifications using approaches such as Hidden Markov Models (HMMs) ( Grecian et al., 2018; McClintock & Michelot, 2018) and Expectation Maximisation Binary Clustering (EMBC) (Garriga et al., 2016), and also to derive time-activity budgets and insight into changes in energetic budgets following construction of offshore wind farms, reducing uncertainty surrounding the predicted consequences of displacement and barrier effects (OUTCOME 2). By collecting data from multiple individuals and across multiple colonies, it will also be possible to obtain a greater understanding of variation in behaviour, energetics and links between individual condition and demography, improving our understanding of population-level impacts on the populations concerned (OUTCOME 3). Finally, through the analyses of data from tagged birds when within or in the vicinity of operational wind farms (e.g. Beatrice, Hywind, European Offshore Wind Deployment Centre) it will be possible to better understand how birds respond to wind farms, helping to determine how impacts can be reduced through possible mitigation measures and through careful marine spatial planning (OUTCOME 4).



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