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dc.contributor.authorSmout, Sophie
dc.contributor.authorMurray, Kimberly
dc.contributor.authorAarts, Geert
dc.contributor.authorBiuw, Martin
dc.contributor.authorBrasseur, Sophie
dc.contributor.authorBuren, Alejandro
dc.contributor.authorEmpacher, Fanny
dc.contributor.authorFrie, Anne Kirstine
dc.contributor.authorGrecian, James
dc.contributor.authorHammill, Mike
dc.contributor.authorMikkelsen, Bjarni
dc.contributor.authorMosnier, Arnaud
dc.contributor.authorRosing-asvid, Aqqalu
dc.contributor.authorRussell, Debbie
dc.contributor.authorSkaug, Hans
dc.contributor.authorStenson, Garry
dc.contributor.authorThomas, Len
dc.contributor.authorVer Hoef, Jay
dc.contributor.authorWitting, Lars
dc.contributor.authorZabavnikov, Vladimir
dc.contributor.authorØigård, Tor Arne
dc.contributor.authorFernandez, Ruth
dc.contributor.authorWickson, Fern
dc.date.accessioned2021-07-16T14:30:05Z
dc.date.available2021-07-16T14:30:05Z
dc.date.issued2021-05-18
dc.identifier.citationSmout , S , Murray , K , Aarts , G , Biuw , M , Brasseur , S , Buren , A , Empacher , F , Frie , A K , Grecian , J , Hammill , M , Mikkelsen , B , Mosnier , A , Rosing-asvid , A , Russell , D , Skaug , H , Stenson , G , Thomas , L , Ver Hoef , J , Witting , L , Zabavnikov , V , Øigård , T A , Fernandez , R & Wickson , F 2021 , ' Report of the NAMMCO-ICES Workshop on Seal Modelling (WKSEALS 2020) ' , NAMMCO Scientific Publications , vol. 12 , no. 1 , 5794 . https://doi.org/10.7557/3.5794en
dc.identifier.issn1560-2206
dc.identifier.otherPURE: 274676521
dc.identifier.otherPURE UUID: b6624696-58bb-47c5-933b-c2c7e5b81b8c
dc.identifier.othercrossref: 10.7557/3.5794
dc.identifier.otherORCID: /0000-0002-1969-102X/work/95772345
dc.identifier.otherORCID: /0000-0002-7436-067X/work/95772374
dc.identifier.otherORCID: /0000-0002-6428-719X/work/95772725
dc.identifier.otherScopus: 85165486366
dc.identifier.urihttps://hdl.handle.net/10023/23596
dc.description.abstractTo support sustainable management of apex predator populations, it is important to estimate population size and understand the drivers of population trends to anticipate the consequences of human decisions. Robust population models are needed, which must be based on realistic biological principles and validated with the best available data. A team of international experts reviewed age-structured models of North Atlantic pinniped populations, including Grey seal (Halichoerus grypus), Harp seal (Pagophilus groenlandicus), and Hooded seal (Cystophora cristata). Statistical methods used to fit such models to data were compared and contrasted. Differences in biological assumptions and model equations were driven by the data available from separate studies, including observation methodology and pre-processing. Counts of pups during the breeding season were used in all models, with additional counts of adults and juveniles available in some. The regularity and frequency of data collection, including survey counts and vital rate estimates, varied. Important differences between the models concerned the nature and causes of variation in vital rates (age-dependent survival and fecundity). Parameterisation of age at maturity was detailed and time-dependent in some models and simplified in others. Methods for estimation of model parameters were reviewed and compared. They included Bayesian and maximum likelihood (ML) approaches, implemented via bespoke coding in C, C++, TMB or JAGS. Comparative model runs suggested that as expected, ML-based implementations were rapid and computationally efficient, while Bayesian approaches, which used MCMC or sequential importance sampling, required longer for inference. For grey seal populations in the Netherlands, where preliminary ML-based TMB results were compared with the outputs of a Bayesian JAGS implementation, some differences in parameter estimates were apparent. For these seal populations, further investigations are recommended to explore differences that might result from the modelling framework and model-fitting methodology, and their importance for inference and management advice. The group recommended building on the success of this workshop via continued collaboration with ICES and NAMMCO assessment groups, as well as other experts in the marine mammal modelling community. Specifically, for Northeast Atlantic harp and hooded seal populations, the workshop represents the initial step towards a full ICES benchmark process aimed at revising and evaluating new assessment models.
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofNAMMCO Scientific Publicationsen
dc.rightsCopyright © 2021 Sophie Smout, Kimberly Murray, Geert Aarts, Martin Biuw, Sophie Brasseur, Alejandro Buren, Fanny Empacher, Anne Kirstine Frie, James Grecian, Mike Hammill, Bjarni Mikkelsen, Arnaud Mosnier, Aqqalu Rosing-Asvid, Debbie Russell, Hans Skaug, Garry Stenson, Len Thomas, Jay ver Hoef, Lars Witting, Vladimir Zabavnikov, Tor Arne Øigård, Ruth Fernandez, Fern Wickson This work is licensed under a Creative Commons Attribution 4.0 International License.en
dc.subjectPinnipeden
dc.subjectManagementen
dc.subjectPopulation dynamics modellingen
dc.subjectBayesian maximum likelihooden
dc.subjectAge-structureden
dc.subjectPup productionen
dc.subjectMCMCen
dc.subjectSequential importance samplingen
dc.subjectParticle filteren
dc.subjectGC Oceanographyen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subjectSDG 14 - Life Below Wateren
dc.subject.lccGCen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.titleReport of the NAMMCO-ICES Workshop on Seal Modelling (WKSEALS 2020)en
dc.typeJournal articleen
dc.contributor.sponsorNERCen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Coastal Resources Management Groupen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doihttps://doi.org/10.7557/3.5794
dc.description.statusPeer revieweden
dc.identifier.grantnumberNE/R015007/1en


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