Deep Data: Modeling Sixgill Shark Movements

Abstract

Movement is an essential question of ecology which impacts community connectivity and structure, and has implications for management. In the ocean tracking animal movements has unique challenges. For deep organisms, technological limitations require unique solutions to analyzing and obtaining data. Sixgill sharks (Hexanchus griseus) are one such organism, with very little known about their behavior – in situ sightings are rare and require submersibles. To understand their movements we must use remote methods, such as acoustic tagging, and pair those data with environmental information. Here, we present the first steps at such a project, pairing location data with the temporally varying environmental variables of temperature and salinity. Our results establish that acoustic technologies can extend analytical approaches common to terrestrial systems to the management and conservation of marine organisms.

Date
Location
Spark 227, Washington State University
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