A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea

dc.contributor.authorPlagányi, Éva E
dc.contributor.authorButterworth, Doug S
dc.date.accessioned2016-03-31T09:14:08Z
dc.date.available2016-03-31T09:14:08Z
dc.date.issued2006
dc.date.updated2016-03-31T09:13:07Z
dc.description.abstractA Spatial Multi-species Operating Model (SMOM) of the underlying krill-predator-fishery dynamics is developed in response to requests for scientific advice regarding the subdivision of the precautionary catch limit for krill among 15 small-scale management units (SSMUs) in the Scotia Sea to reduce the potential impact of fishing on land-based predators. The model is intended to complement the outputs from the KPFM. The model includes all 15 SSMUs and uses an annual timestep to update the numbers of krill in each of the SSMUs, as well as the numbers of predator species in each of these areas. The model currently includes only two predator groups (penguins and seals) but is configured so that there is essentially no upper limit on the number of predator species which can be included. Given the numerous uncertainties regarding the choice of parameter values, a Reference Set is used in preference to a single Reference Case operating model. The initial Reference Set used comprises 12 alternative combinations that essentially try to bound the uncertainty in the choice of survival estimates as well as the breeding success relationship. The model is coded in AD Model Builder and quickly generates large numbers of stochastic replicates to explore different hypotheses such as that related to the transport of krill. The SMOM developed here is intended for use as an operating model in a formal MP framework described in an accompanying paper. Different MPs are simulation tested with their performances being compared on the basis of an agreed set of performance statistics which essentially compare the risks of reducing the abundance of predators below certain levels, as well as comparing the variability in future average krill catches per SSMU associated with each MP.en_ZA
dc.identifier.apacitationPlagányi, É. E., & Butterworth, D. S. (2006). <i>A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea</i> University of Cape Town ,Faculty of Science ,Marine Resource Assessment and Management Group. Retrieved from http://hdl.handle.net/11427/18440en_ZA
dc.identifier.chicagocitationPlagányi, Éva E, and Doug S Butterworth <i>A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea.</i> University of Cape Town ,Faculty of Science ,Marine Resource Assessment and Management Group, 2006. http://hdl.handle.net/11427/18440en_ZA
dc.identifier.citationPlagányi, É., & Butterworth, D.S (2006). A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea.en_ZA
dc.identifier.ris TY - Working Paper AU - Plagányi, Éva E AU - Butterworth, Doug S AB - A Spatial Multi-species Operating Model (SMOM) of the underlying krill-predator-fishery dynamics is developed in response to requests for scientific advice regarding the subdivision of the precautionary catch limit for krill among 15 small-scale management units (SSMUs) in the Scotia Sea to reduce the potential impact of fishing on land-based predators. The model is intended to complement the outputs from the KPFM. The model includes all 15 SSMUs and uses an annual timestep to update the numbers of krill in each of the SSMUs, as well as the numbers of predator species in each of these areas. The model currently includes only two predator groups (penguins and seals) but is configured so that there is essentially no upper limit on the number of predator species which can be included. Given the numerous uncertainties regarding the choice of parameter values, a Reference Set is used in preference to a single Reference Case operating model. The initial Reference Set used comprises 12 alternative combinations that essentially try to bound the uncertainty in the choice of survival estimates as well as the breeding success relationship. The model is coded in AD Model Builder and quickly generates large numbers of stochastic replicates to explore different hypotheses such as that related to the transport of krill. The SMOM developed here is intended for use as an operating model in a formal MP framework described in an accompanying paper. Different MPs are simulation tested with their performances being compared on the basis of an agreed set of performance statistics which essentially compare the risks of reducing the abundance of predators below certain levels, as well as comparing the variability in future average krill catches per SSMU associated with each MP. DA - 2006 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2006 T1 - A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea TI - A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea UR - http://hdl.handle.net/11427/18440 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/18440
dc.identifier.vancouvercitationPlagányi ÉE, Butterworth DS. A Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Sea. 2006 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/18440en_ZA
dc.languageengen_ZA
dc.publisher.departmentMarine Resource Assessment and Management Groupen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherSpatial Multi-species Operating Model
dc.subject.otherkrill
dc.titleA Spatial Multi-species Operating Model (SMOM) of krill–predator interactions in small-scale management units in the Scotia Seaen_ZA
dc.typeWorking Paperen_ZA
uct.type.filetypeText
uct.type.publicationResearchen_ZA
uct.type.resourceResearch paperen_ZA
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