Assessing Marine Mammal Abundance: A Novel Data Fusion

TL;DR

This paper introduces a novel data fusion method combining aerial distance sampling and passive acoustic monitoring to improve estimates of North Atlantic right whale abundance and distribution, overcoming limitations of individual data sources.

Contribution

The paper presents a new data fusion approach that integrates two different observation modalities to enhance marine mammal abundance estimation.

Findings

Improved inference of whale abundance and distribution.

Performance gains demonstrated through simulation and real data.

Effective combination of spatial and acoustic data sources.

Abstract

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.