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dc.contributor.advisorPunt, Andre Een_US
dc.contributor.authorFay, Gavinen_US
dc.date.accessioned2012-09-13T17:32:37Z
dc.date.available2012-09-13T17:32:37Z
dc.date.issued2012-09-13
dc.date.submitted2012en_US
dc.identifier.otherFay_washington_0250E_10692.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/20743
dc.descriptionThesis (Ph.D.)--University of Washington, 2012en_US
dc.description.abstractAccounting for spatial complexity provides a diversity of challenges for natural resource management. Challenges arise from uncertainties in spatial stock structure, heterogeneity in environmental and ecological constraints, and from spatial differences in exploitation and management actions. Simulation frameworks were used to analyse methods that account for spatial heterogeneity when (1) identifying spatial population trends, (2) evaluating the performance of monitoring designs among multiple sites, (3) estimating spatial distribution of sea lion prey, (4) examining energetic implications of foraging strategies given uncertainty, and (5) determining how spatial dynamics of exploited populations and fishing fleets affects performance of harvest strategies. Linear state-space models using the Kalman filter were developed to estimate trends in pup production for Steller sea lions (Eumetopias jubatus). Models assuming spatial correlation in trend among rookeries were more robust to stock structure assumptions, and estimated trends and abundance even given missing data. Relative gains in performance when optimising monitoring designs for pup production were evaluated using Canonical Correspondence Analysis and by assessing how frequently optimal designs out-performed random designs. Optimal allocation of monitoring effort depended on the metric of interest. Poorest performance occurred with suboptimal designs for current trend and individual rookery numbers. Bayesian hierarchical models were used to characterise the spatial distribution of fish species in the Gulf of Alaska. Spatial autocorrelation was prevalent in all species, with estimates of abundance generally lower than those obtained using models ignoring spatial correlation. Individual-based modelling of sea lion foraging examined how spatial persistence of prey and the choice of foraging strategy impacted the ability of foragers to meet energetic requirements. Tradeoffs and interactions were observed among model components, successful strategies involved either low uncertainty about prey distribution, or placed substantial emphasis on previous experience. The performance of harvest strategies for the blue eye trevalla (Hyperoglyphe antarctica) fishery in southeast Australia was evaluated using Management Strategy Evaluation. Appropriate weighting of spatial data was required to meet management objectives, although uncertainties regarding natural mortality and stock-recruitment steepness dominated variation in performance. In summary, spatial variability ought to be considered when modelling and managing marine resources, however appropriate scales of response are necessary.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectBlue eye trevalla; Kalman filter; Management Strategy Evaluation; Simulation modelling; Spatial variability; Steller sea lionen_US
dc.subject.otherFisheries and aquatic sciencesen_US
dc.subject.otherNatural resource managementen_US
dc.subject.otherConservation biologyen_US
dc.subject.otherFisheriesen_US
dc.titleSpatial modelling for monitoring and management of marine metapopulationsen_US
dc.typeThesisen_US
dc.embargo.termsNo embargoen_US


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