Data from: Modelling flight heights of Lesser Black-backed Gulls and Great Skuas from GPS: a Bayesian approach

1. Wind energy generation is increasing globally and associated environmental impacts must be considered. The risk of seabirds colliding with offshore wind turbines is influenced by flight height, and flight height data usually come from observers on boats, making estimates in daylight in fine weather. GPS tracking provides an alternative and generates flight height information in a range of conditions, but the raw data have associated error.
2. Here we present a novel analytical solution for accommodating GPS error. We use Bayesian state-space models to describe the flight height distributions and the error in altitude measured by GPS for Lesser Black-backed Gull and Great Skua, tracked throughout the breeding season. We also examine how location and light levels influence flight height.
3. Lesser Black-backed Gulls flew lower by night than by day, indicating that this species would be less likely to encounter turbine blades at night, when birds’ ability to detect and avoid them might be reduced. Gulls flew highest over land and lowest near the coast. For Great Skuas, no significant relationships were found between flight height, time of day and location.
4. We consider four ‘collision risk windows’, corresponding to the airspace swept by rotor blades for different offshore wind turbine designs. We found the highest proportion of birds at risk for a 22-250 m turbine (up to 9% for Great Skuas and 34% for Lesser Black-backed Gulls) and the lowest for a 30-258 m turbine. Our results suggest Lesser Black-backed Gulls are at greater risk of collision than Great Skuas, especially by day.
5. Synthesis and applications. Our novel modelling approach is a powerful and effective way of resolving the error associated with avian GPS tracking data. We demonstrate its use on GPS measurements of altitude, generating important information on how breeding seabirds use their environment; specifically, how time of day and location affect flight heights. This approach and the associated data will provide additional information to improve avian collision risk assessments for offshore wind farm developments.15-Jul-2016

1. 全球风能发电量持续攀升，其关联的环境影响不容忽视。海鸟与海上风力涡轮机（offshore wind turbines）相撞的风险受飞行高度影响，而现有飞行高度数据通常来源于船舶观察员，仅能在晴朗天气的日间开展估算。GPS追踪（GPS tracking）作为替代方案，可在多种环境条件下获取飞行高度信息，但原始数据存在伴随误差。
2. 本研究提出一种全新的解析方案，以适配GPS追踪带来的误差。我们采用贝叶斯状态空间模型（Bayesian state-space models），对繁殖季全程追踪的小黑背鸥（Lesser Black-backed Gull）与大贼鸥（Great Skua）的飞行高度分布，以及GPS测得的海拔误差进行建模分析。同时，我们还探讨了地理位置与光照水平对飞行高度的影响。
3. 小黑背鸥夜间飞行高度低于日间，这表明该物种夜间遭遇涡轮机叶片碰撞的概率更低——此时鸟类侦测并规避叶片的能力可能有所下降。鸥类在陆地上空飞行高度最高，近岸区域最低。而大贼鸥的飞行高度与日间时段、地理位置均未呈现显著关联。
4. 我们针对四类"碰撞风险窗口"展开分析，对应不同海上风力涡轮机设计的桨叶扫掠空域。研究发现，22-250米规格的涡轮机对应的鸟类碰撞风险占比最高（大贼鸥最高可达9%，小黑背鸥最高可达34%），而30-258米规格的涡轮机对应的风险占比最低。结果表明，小黑背鸥的碰撞风险高于大贼鸥，日间尤为显著。
5. 综合与应用。本研究提出的新型建模方法，可有效解决鸟类GPS追踪数据的误差问题。我们将该方法应用于海拔GPS测量数据，揭示了繁殖季海鸟的栖息地利用模式，具体包括日间时段与地理位置对飞行高度的影响。该方法及配套数据将为海上风力发电场开发中的鸟类碰撞风险评估提供额外支撑信息。2016年7月15日