{"refrec":{"BRefID":356238,"RR":"<b>Lavender, E.; Aleynik, D.; Dodd, J.; Illian, J.; James, M.; Smout, S.; Thorburn, J.</b> (2022). Benthic animal-borne sensors and citizen science combine to validate ocean modelling. <i>NPG Scientific Reports 12(1)</i>: 16613. <a href=\"https://dx.doi.org/10.1038/s41598-022-20254-z\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-022-20254-z</a>","BEntID":353952,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 12(1)</i>: 16613. <a href=\"https://dx.doi.org/10.1038/s41598-022-20254-z\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-022-20254-z</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Lavender, E.; Aleynik, D.; Dodd, J.; Illian, J.; James, M.; Smout, S.; Thorburn, J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Lavender, E. <i>et al.</i>","Englishabstract":"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 (<i>Dipturus intermedius</i>) 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&nbsp;°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.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Benthic animal-borne sensors and citizen science combine to validate ocean modelling","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-20 01:32:53.047812","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2022,"MonPub":null,"DateUpdate":"2022-10-11","DateCreate":"2022-10-11","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000864277100013","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-022-20254-z"},"refs":null,"anarec":{"AnaID":356238,"PubliDate":2022,"Pagination":"16613","XtraPublOfAnaID":null,"ISBN":null,"Volume":"12","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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