Impacts of spatial uncertainty on performance of age structure-based harvest strategies for blue eye trevalla (Hyperoglyphe antarctica)

Harvest control rules (HCRs), key components of fisheries management strategies, are used to calculate recommended catch levels given estimates of present stock biomass or levels of fishing mortality. Spatial variability, either in the population dynamics, fishery operations, or in data collection, has the potential to impact HCR performance as this can drive variability in indicators used for stock assessment. A management strategy evaluation (MSE) approach was used to evaluate the performance of HCRs for blue eye trevalla (Hyperoglyphe antarctica), a species that exhibits spatial variability in monitoring data in southeast Australia. Employing catch curve analysis, the HCRs rely solely on information from the age structure of the catch. Several versions of the HCRs were tested, varying the reference points used to determine management actions, and the way spatial variability was accounted for when setting catch limits. The results suggest that effective implementation of the HCRs is challenging, requiring appropriate choice of reference points and estimators. Spatial disaggregation of data leads to imprecise estimates of mortality rates. However, appropriate weighting of spatial estimates of stock status leads to higher relative stock size than when data are aggregated spatially. Variation in performance measures is dominated by uncertainty associated with mis-specification of the rate of natural mortality and the steepness of the stock–recruitment relationship. Such uncertainties can be expected for an information-poor, spatially heterogeneous resource, therefore additional considerations besides the HCR should be taken to achieve a desired precautionary result in contrast to the situation for more data-rich scenarios.

► Spatial structure can impact indicators used for fisheries stock assessment.
► We used simulation methods to test harvest control rules for blue eye trevalla.
► Effective operation required appropriate choice of reference points and estimators.
► Spatially weighted mortality estimates resulted in the largest relative stock size.
► Mis-specifying biological parameters drove variability in performance measures.