Can a spatially-structured stock assessment address uncertainty due to closed areas? A case study based on pink ling in Australia

Spatial structure in biological characteristics and exploitation rates impact the performance of stock assessment methods used to estimate the status of fish stocks relative to target and limit reference points. Spatially-structured stock assessment methods can reduce the bias and imprecision in the estimates of management-related model outputs. However, their performance has only recently been evaluated formally, in particular when some of the area fished is closed. In order to evaluate the effects of closed areas and spatial variation in growth and exploitation rate when estimating spawning biomass, a spatially-explicit operating model was developed to simulate spatial data, and five configurations of the stock assessment package Stock Synthesis (three of which were spatially structured) were applied. The bias in estimates of spawning stock biomass associated with spatially-aggregated assessment methods increases in the presence of closed areas while these biases can be reduced (or even eliminated) by applying appropriately constructed spatially-structured stock assessments. The performance of spatially-aggregated assessments when estimating spawning stock biomass is found to depend on the interactions among spatial variation in growth, in exploitation rate, and in knowledge of the spatial areas over which growth and exploitation rate are homogeneous. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.