Renewable energy continues to grow globally, and the number of offshore wind farms is set to increase. Whilst wind energy developments provide energy security and reduced carbon budgets, they may impact bird populations through collision mortality, habitat modification and avoidance. To date, avian collision mortality has received the most attention and collision risk models have been developed to estimate the potential mortality caused by wind turbines. The utility of these models relies not only on their underlying assumptions but also on the data available to ensure the predictions are informative. Using a stochastic collision risk model (sCRM; based on the Band collision risk model) as an example, we explore the importance of bird flight speed and consider how the assumptions of the model influence the sensitivity to flight speed. Furthermore we explore the consequences of using site-specific GPS-derived flight speed rather than a standard generic value, with Lesser Black-backed Gulls Larus fuscus as an example, and consider how this generic value is currently used. We found that the model was most sensitive to the parameters of bird density, non-avoidance rate and percentage of birds at collision risk height, as well as bird flight speed. Using site-specific flight speed data derived from GPS tags rather than a standard value reduced the predicted number of collisions. We highlight that within the model, both the estimation of the probability of collision (PColl) and the flux of birds are sensitive to the bird flight speed; this sensitivity acts in opposite directions but the two do not necessarily balance out. Therefore, when the sCRM is used as generally done, there is little difference in collision estimates if airspeeds (bird flight speed relative to air through which it is moving) are used rather than groundspeeds (bird flight speed relative to ground). Estimates of seabird collision rates in relation to offshore wind farms are impacting future offshore wind development. By using site specific flight speed estimates and, accounting for different speeds in relation to wind direction, we demonstrate that cumulative collision estimates can be affected, highlighting the need for more representative flight speed data and where possible site-specific data.

可再生能源在全球范围内持续增长，海上风电场的数量也将增加。虽然风能开发提供了能源安全并减少了碳预算，但它们可能会通过碰撞死亡率、栖息地改变和避免影响鸟类种群。迄今为止，鸟类碰撞死亡率受到了最多的关注，并且已经开发了碰撞风险模型来估计风力涡轮机造成的潜在死亡率。这些模型的效用不仅依赖于它们的基本假设，还依赖于可用于确保预测提供信息的可用数据。以随机碰撞风险模型（sCRM；基于 Band 碰撞风险模型）为例，我们探讨了鸟类飞行速度的重要性，并考虑模型的假设如何影响对飞行速度的敏感性。红嘴鸥作为示例，并考虑当前如何使用此通用值。我们发现该模型对鸟类密度、非回避率和处于碰撞风险高度的鸟类百分比以及鸟类飞行速度等参数最为敏感。使用来自 GPS 标签的特定站点飞行速度数据而不是标准值减少了预测的碰撞次数。我们强调，在模型中，碰撞概率 (PColl) 的估计和鸟类的通量都对鸟类的飞行速度很敏感；这种敏感性作用于相反的方向，但两者不一定平衡。因此，当像通常那样使用 sCRM 时，如果使用空速（鸟类相对于其移动的空气的飞行速度）而不是地速（鸟类相对于地面的飞行速度），则碰撞估计几乎没有区别。与海上风电场相关的海鸟碰撞率的估计正在影响未来的海上风电开发。通过使用特定地点的飞行速度估计，并考虑与风向相关的不同速度，我们证明了累积碰撞估计会受到影响，强调需要更具代表性的飞行速度数据以及可能的特定地点数据。