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dc.contributor.authorHuse, Geir
dc.contributor.authorGiske, Jarl
dc.date.accessioned2012-10-08T17:33:42Z
dc.date.available2012-10-08T17:33:42Z
dc.date.issued1998
dc.identifier.citationThis report is not to be cited without prior reference to the authorsno_NO
dc.identifier.urihttp://hdl.handle.net/11250/105851
dc.description.abstractDue to spatial heterogeneity, the survival and growth of fishes depend upon their habitat. For mobile pelagic fishes, it hence becomes important to analyse the factors responsible for spatial dynamics in order to understand population dynamics better. The current work is an attempt to approach fisheries assessment from a theoretical ecological perspective. We here present individual based single species and multispecies models where spatial behaviour and life history strategies are evolved. Spatial movement for each fish is calculated using an artificial neural network with weights evolved by a genetic algorithm. Behaviour relies on sensory information about food, temperature, light and predators. A concept for modelling life history strategies and migration in fish using this method is presented along with an application of this concept for the Barents Sea capelin. A predator-prey model based on the same principle is also presented. Potential areas of application for these kinds of models are discussed.no_NO
dc.language.isoengno_NO
dc.publisherICESno_NO
dc.relation.ispartofseriesICES CM Documents;1998/J:16
dc.subjectfish behaviourno_NO
dc.subjectfiskeatferdno_NO
dc.subjectgeneticsno_NO
dc.subjectgenetikkno_NO
dc.subjectdistributionno_NO
dc.subjectutbredelseno_NO
dc.titleIndividual based spatial models with evolved fish behaviourno_NO
dc.typeWorking paperno_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Zoology and botany: 480::Ethology: 485no_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452no_NO
dc.subject.nsiVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923no_NO
dc.source.pagenumber16 s.no_NO


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