Marine mammals are increasingly vulnerable to human disturbance and climate change. Their diving behavior leads to limited visual access during data collection, making studying the abundance and distribution of marine mammals challenging. In theory, using data from more than one observation modality should lead to better informed predictions of abundance and distribution. With focus on North Atlantic right whales, we consider the fusion of two data sources to inform about their abundance and distribution. The first source is aerial distance sampling which provides the spatial locations of whales detected in the region. The second source is passive acoustic monitoring (PAM), returning calls received at hydrophones placed on the ocean floor. Due to limited time on the surface and detection limitations arising from sampling effort, aerial distance sampling only provides a partial realization of locations. With PAM, we never observe numbers or locations of individuals. To address these challenges, we develop a novel thinned point pattern data fusion. Our approach leads to improved inference regarding abundance and distribution of North Atlantic right whales throughout Cape Cod Bay, Massachusetts in the US. We demonstrate performance gains of our approach compared to that from a single source through both simulation and real data.

翻译：海洋哺乳动物日益受到人类干扰和气候变化的威胁。其潜水行为导致数据采集过程中视觉观测受限，使得研究海洋哺乳动物的丰度与分布颇具挑战。理论上，融合多种观测模态的数据能够更精准地预测海洋哺乳动物的丰度与分布。本研究以北大西洋露脊鲸为研究对象，探索融合两种数据源以推断其丰度与分布信息。第一种数据源为航空距离采样，提供区域中鲸类被探测到的空间位置；第二种数据源为被动声学监测（PAM），通过放置在海底的水听器记录接收到的鲸类叫声。由于鲸类在水面停留时间有限，且采样努力量导致探测能力受限，航空距离采样仅能提供部分位置信息。而被动声学监测（PAM）则无法直接观测个体数量或位置。针对上述挑战，我们提出一种新型稀疏点模式数据融合方法。该方法能有效提升对美国马萨诸塞州科德角湾的北大西洋露脊鲸丰度与分布的推断精度。通过模拟实验与真实数据验证，我们证明了该方法相较于单一数据源的性能优势。