Spatial data fusion of point processes to infer marine mammal abundance

With increased data collection, the need to fuse data sources has emerged as an important and rapidly growing research activity in the statistical community. In considering spatial and spatio-temporal datasets to examine complex environmental and ecological processes of interest, we often have multiple sources that are jointly informative about features of interest of the processes. Model-based data fusion aims to leverage information from these sources to improve inference and prediction. In the spatial statistics setting, these data could be geostatistical, areal, or point patterns with varying spatial resolutions, supports, and domains. With focus on North Atlantic right whales, we explore stochastic modeling to implement a suitable fusion to inform about their abundance and distribution with full inference and uncertainty. 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. We demonstrate performance gains of our approach compared to that from a single source through simulation and apply our model to North Atlantic right whale data collected throughout Cape Cod Bay, Massachusetts in the U.S.