ASSESSING MARINE MAMMAL ABUNDANCE: A NOVEL DATA FUSION

• 成果类型: Article
• 署名作者: Schliep, Erin M.; Gelfand, Alan E.; Clark, Christopher W.; Mayo, Charles A.; Mckenna, Brigid; Parks, Susan E.; Yack, Tina M.; Schick, Roberts.
• 署名单位: North Carolina State University; Duke University; Cornell University; Syracuse University; Duke University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1924
• 发表日期: 2024
• 页码: 3071-3090
• 关键词: atlantic right whales; distance sampling data; cape-cod bay; eubalaena-glacialis; spatial models; BEHAVIOR; Massachusetts; inference; SPACE; BIAS

摘要

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 U.S. We demonstrate performance gains of our approach compared to that from a single source through both simulation and real data.