Benthic animal-borne sensors and citizen science combine to validate ocean modelling

Abstract

Abstract Developments in animal electronic tagging and tracking have transformed the field of movement ecology, but interest is also growing in the contributions of tagged animals to oceanography. Animal-borne sensors can address data gaps, improve ocean model skill and support model validation, but previous studies in this area have focused almost exclusively on satellite-telemetered seabirds and seals. Here, for the first time, we develop the use of benthic species as animal oceanographers by combining archival (depth and temperature) data from animal-borne tags, passive acoustic telemetry and citizen-science mark-recapture records from 2016–17 for the Critically Endangered flapper skate (Dipturus intermedius) in Scotland. By comparing temperature observations to predictions from the West Scotland Coastal Ocean Modelling System, we quantify model skill and empirically validate an independent model update. The results from bottom-temperature and temperature-depth profile validation (5,324 observations) fill a key data gap in Scotland. For predictions in 2016, we identified a consistent warm bias (mean = 0.53 °C) but a subsequent model update reduced bias by an estimated 109% and improved model skill. This study uniquely demonstrates the use of benthic animal-borne sensors and citizen-science data for ocean model validation, broadening the range of animal oceanographers in aquatic environments.