Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, UK.
Scottish Oceans Institute, University of St Andrews, St Andrews, UK.
Sci Rep. 2022 Oct 5;12(1):16613. doi: 10.1038/s41598-022-20254-z.
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.
动物电子标记和追踪技术的发展已经改变了运动生态学领域，但人们对标记动物对海洋学的贡献的兴趣也在增长。动物携带的传感器可以解决数据空白问题，提高海洋模型的技能并支持模型验证，但该领域的先前研究几乎完全集中在卫星遥测的海鸟和海豹上。在这里，我们首次通过结合 2016-17 年从苏格兰濒危的 Flapper 鳐（Dipturus intermedius）上获得的动物携带标签、被动声学遥测和公民科学标记重捕记录中的档案（深度和温度）数据，将底栖物种开发为动物海洋学家。通过将温度观测与来自西苏格兰沿海海洋建模系统的预测进行比较，我们量化了模型技能并对独立的模型更新进行了经验验证。在苏格兰，底部温度和温度深度剖面验证的结果（5324 次观测）填补了一个关键的数据空白。对于 2016 年的预测，我们发现存在一致的暖偏差（平均值为 0.53°C），但随后的模型更新将偏差减少了约 109%，并提高了模型技能。这项研究独特地展示了使用底栖动物携带传感器和公民科学数据进行海洋模型验证，拓宽了水生环境中动物海洋学家的范围。
