A simulated archival tagging programme for albacore (thunnus alalunga) in the northeast atlantic, including an analysis of factors affecting tag recovery
Ortiz de Zarate, V.
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Cosgrove, R. Arregi, I.; Brophy, D.; Arrizabalaga, H.; Ortiz de Zarate, V.; Griffin, N. (2010). A simulated archival tagging programme for albacore (thunnus alalunga) in the northeast atlantic, including an analysis of factors affecting tag recovery. ICES Journal of Marine Science 67 (6), 1216-1221
Cosgrove, R., Arregi, I., Brophy, D., Arrizabalaga, H., Ortiz de Zarate, V., and Griffin, N. 2010. A simulated archival tagging programme for albacore (Thunnus alalunga) in the Northeast Atlantic, including an analysis of factors affecting tag recovery. - ICES Journal of Marine Science, 67: 1216-1221. An archival tagging programme in the Northeast Atlantic would assist in testing the hypothesis that subpopulations of juvenile albacore exist within the region and provide important information on fish behaviour in relation to environmental variables. No information was available, however, on the ability of juvenile albacore to carry costly implanted archival tags, or on rates of tag recovery. A simulated archival tagging study on albacore using simulated or &quot;dummy&quot; archival tags was therefore carried out in the Bay of Biscay from 2005 to 2008. In all, 353 fish were tagged and released, and 9 fish (2.55%) were recaptured. A comprehensive ICCAT database of conventionally tagged fish in the Northeast Atlantic was also analysed to determine whether optimal conditions at the time of release could be identified and used to boost recovery rates in future tagging programmes. A binary logistic regression model using a response variable with two possible outcomes, recaptured or not recaptured, was developed, then tested on two datasets to deal with association between variables. Effort and fishing gear were significant in the first dataset, and length class and fishing gear in the second. The last two factors can be manipulated, and a recapture rate of &gt;5% was predicted if derived optimal tagging conditions are followed in future tagging programmes.