Big Data, Sound Science, Lasting Impact: a framework for passive acoustic monitoring

Abstract

Marine passive acoustic monitoring (PAM) has produced petabytes of data that are used by researchers, resource managers, industry, and regulators to understand how marine animals use sound and the impacts of anthropogenic noise on species and ecosystems throughout the global ocean. These big data provide unprecedented opportunities to study underwater soundscapes and marine ecology but also enormous challenges to efficiently extract information. To address these challenges, a U.S. federally funded and led Sound Cooperative (SoundCoop) project built community-focused cyberinfrastructure to promote improved, scalable and sustainable processing and access of marine PAM data for management, science, industry and military applications. Driven by cross-institutional participation representing a diversity of data collection methods and conditions, the SoundCoop project established guidance for standardized processing of ocean sound level metrics using freeware software toolkits and developed core tools and processes that support open science. Four examples of comparative analyses that connect disparate PAM monitoring efforts, and integrate non-acoustic data illustrate how comparable, interoperable sound level metrics support a more coherent and synoptic perspective on global ocean soundscapes using methods that current and future PAM projects can leverage. Such a framework around PAM big data offers the opportunity to revolutionize large-scale marine ecology and oceanography in similar ways to other transformative approaches for understanding environmental or ecological patterns and processes at global scales.