Data from: Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins

Fine-scale information on the occurrence of coastal cetaceans is required to support regulation of offshore energy developments and marine spatial planning. In particular, the EU Habitats Directive requires an understanding of the extent to which animals from Special Areas of Conservation (SAC) use adjacent waters, where survey effort is often sparse. Designing survey regimes that can be used to support these assessments is especially challenging because visual sightings are expected to be rare in peripheral parts of a population's range. Consequently, even intensive visual line-transect surveys can result in few encounters. Static passive acoustic monitoring (PAM) provides new opportunities to extend survey effort by using echolocation click detections to quantify levels of occurrence of coastal dolphins, but this does not provide information on species identity. In NE Scotland, assessments of proposed offshore energy developments required information on spatial patterns of occurrence of bottlenose dolphins in waters in and next to the Moray Firth SAC. Here, we illustrate how this can be achieved by integrating data from broad-scale PAM arrays with presence-only data from visual surveys. Generalized estimating equations were used with PAM data to model the occurrence of dolphins in relation to depth, distance to coast, slope, and sediment, and to predict the spatial variation in the cumulative occurrence of all dolphin species across a 4 × 4 km grid of the study area. Classification tree analysis was then applied to available visual sightings data to estimate the likely species identity of dolphins sighted in each grid cell in relation to local habitat. By multiplying these probabilities, it was possible to provide advice on spatial variation in the probability of encountering bottlenose dolphins from this protected population at a regional scale, complementing data from surveys that estimate average density or overall abundance within a region.

为支撑近海能源开发监管与海洋空间规划工作，亟需获取沿海鲸类物种出现情况的精细化信息。具体而言，《欧盟生境指令》（EU Habitats Directive）要求明确特别保育区（Special Areas of Conservation, SAC）内的物种利用周边水域的范围，而该区域的调查工作往往较为匮乏。由于种群分布外围区域的目视目击记录通常较为稀少，因此设计可支撑此类评估的调查方案极具挑战性。即便实施高密度目视线样调查，也仅能获得少量目击记录。静态被动声学监测（Static Passive Acoustic Monitoring, PAM）为拓展调查覆盖范围提供了新途径：可通过检测回声定位咔哒声量化沿海海豚的出现频次，但该方法无法提供物种身份信息。在苏格兰东北部，针对拟议近海能源开发项目的评估工作，需要获取莫雷湾特别保育区及其周边水域内瓶鼻海豚的空间分布模式信息。本研究展示了如何通过整合大尺度被动声学监测阵列数据与仅含目击存在信息的目视调查数据，来实现这一评估目标。研究团队采用广义估计方程（Generalized Estimating Equations, GEE）结合被动声学监测数据，针对海豚出现情况与水深、离岸距离、地形坡度及沉积物类型的关联关系构建模型，并在研究区域的4×4公里网格尺度下，预测所有海豚物种累计出现情况的空间变异。随后，研究人员针对现有目视目击数据开展分类树分析，以估算各网格单元内目击海豚的物种归属概率，并结合局地生境特征进行关联。通过将两类概率相乘，本研究得以在区域尺度上提供该保护种群瓶鼻海豚的目击概率空间分布建议，以此补充仅能估算区域内平均密度或总丰度的传统调查数据。