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dc.contributor.authorDunlop, K.M.
dc.contributor.authorRuxton, G.D.
dc.contributor.authorScott, E.M.
dc.contributor.authorBailey, D.M.
dc.date.accessioned2016-08-18T15:30:18Z
dc.date.available2016-08-18T15:30:18Z
dc.date.issued2015-11
dc.identifier.citationDunlop , K M , Ruxton , G D , Scott , E M & Bailey , D M 2015 , ' Absolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field data ' , Journal of Experimental Marine Biology and Ecology , vol. 472 , pp. 126-134 . https://doi.org/10.1016/j.jembe.2015.07.010en
dc.identifier.issn0022-0981
dc.identifier.otherPURE: 209172687
dc.identifier.otherPURE UUID: b206d163-78d4-4b5c-92b2-7ed3ed175a4d
dc.identifier.otherScopus: 84938151856
dc.identifier.otherORCID: /0000-0001-8943-6609/work/60427450
dc.identifier.otherWOS: 000360875000014
dc.identifier.urihttp://hdl.handle.net/10023/9328
dc.descriptionThis research was supported by a University of Glasgow Faculty Scholarship to KMD, a Collaborative Gearing Scheme grant from the Natural Environmental Research Council and the British Antarctic Survey (CGS-77) and an ASSEMBLE infrastructure access grant to DMB.en
dc.description.abstractBaited underwater cameras are becoming a popular tool to monitor fish and invertebrate populations within protected and inshore environments where trawl surveys are unsuitable. Modelling the arrival times of deep-sea grenadiers using an inverse square relationship has enabled abundance estimates, comparable to those from bottom trawl surveys, to be gathered from deep-sea baited camera surveys. Baited underwater camera systems in the shallow water environments are however, currently limited to relative comparisons of assemblages based on simple metrics such as MaxN (maximum number of fish seen at any one time). This study describes a stochastic simulation approach used to model the behaviour of fish and invertebrates around a BUC system to enable absolute abundance estimates to be generated from arrival patterns. Species-specific models were developed for the tropical reef fishes the black tip grouper (Epinephelus fasciatus) and moray eel (Gymnothorax spp.) and the Antarctic scavengers; the asteroid (Odontaster validus) and the nemertean worm (Parbolasia corrugatus). A sensitivity analysis explored the impact of input parameters on the arrival patterns (MaxN, time to the arrival of the first individual and the time to reach MaxN) for each species generated by the model. Sensitivity analysis showed a particularly strong link between MaxN and abundance indicating that this model could be used to generate absolute abundances from existing or future MaxN data. It in effect allows the slope of the MaxN vs. abundance relationship to be estimated. Arrival patterns generated by each model were used to estimate population abundance for the focal species and these estimates were compared to data from underwater visual census transects. Using a Bland-Altman analysis, baited underwater camera data processed using this model were shown to generate absolute abundance estimates that were comparable to underwater visual census data.
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofJournal of Experimental Marine Biology and Ecologyen
dc.rightsCopyright © 2015 Elsevier B.V. All rights reserved. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1016/j.jembe.2015.07.010en
dc.subjectBaited underwater camerasen
dc.subjectModellingen
dc.subjectFish and invertebrate surveysen
dc.subjectUnderwater visual censusen
dc.subjectQH301 Biologyen
dc.subject.lccQH301en
dc.titleAbsolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field dataen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1016/j.jembe.2015.07.010
dc.description.statusPeer revieweden


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