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

• Main Authors: Plagányi, Éva E, Butterworth, Doug S
• Format: Report
• Language: English
• Published: Marine Resource Assessment and Management Group 2006
• Subjects: Spatial Multi-species Operating Model; krill; Scotia Sea
• Online Access: http://hdl.handle.net/11427/18440

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.