Spatial modelling for monitoring and management of marine metapopulations

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Spatial modelling for monitoring and management of marine metapopulations

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dc.contributor.advisor Punt, Andre E en_US
dc.contributor.author Fay, Gavin en_US
dc.date.accessioned 2012-09-13T17:32:37Z
dc.date.available 2012-09-13T17:32:37Z
dc.date.issued 2012-09-13
dc.date.submitted 2012 en_US
dc.identifier.other Fay_washington_0250E_10692.pdf en_US
dc.identifier.uri http://hdl.handle.net/1773/20743
dc.description Thesis (Ph.D.)--University of Washington, 2012 en_US
dc.description.abstract Accounting 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.mimetype application/pdf en_US
dc.language.iso en_US en_US
dc.subject Blue eye trevalla; Kalman filter; Management Strategy Evaluation; Simulation modelling; Spatial variability; Steller sea lion en_US
dc.subject.other Fisheries and aquatic sciences en_US
dc.subject.other Natural resource management en_US
dc.subject.other Conservation biology en_US
dc.subject.other Fisheries en_US
dc.title Spatial modelling for monitoring and management of marine metapopulations en_US
dc.type Thesis en_US
dc.embargo.terms No embargo en_US


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