Spatial bias in pāua Haliotis iris catch-per-unit-effort

Citation

Neubauer, P. (2017). Spatial bias in pāua Haliotis iris catch-per-unit-effort. New Zealand Fisheries Assessment Report, 2017/57. Retrieved from http://www.mpi.govt.nz/dmsdocument/23272-far-201757-spatial-bias-in-paua-cpue

Summary

In fisheries, the exploitation of spatially discrete populations can lead to decoupling of trends in fishery-dependent catch-per-unit-effort (CPUE) and actual stock biomass trends. In this context, abalone (Halitidae) CPUE is regarded as unreliable in many abalone fisheries worldwide, where stocks are made up of a number of populations with differing biological characteristics.

In New Zealand, however, CPUE still provides the basis for blacklip abalone (pāua; Haliotis Iris) stock assessments, despite reliable evidence that management areas contain a mosaic of sub-populations with differing demographic parameters. This assessment approach is of particular concern for pāua management area 7 (PAU 7), in which CPUE has been declining in recent years despite efforts by the fishing industry to rebuild the stock with voluntary reductions in allowable catch.

In this study, a simulation model was developed to investigate how CPUE indices, as estimated from linear models, are influenced by spatial use patterns and by management intervention in the form of reduced catch allowances. Simulations confirmed that spatially-variable resource use, determined by variable productivity in space, can introduce large bias in estimated CPUE indices. I also found predictable bias in the form of hyper-depleted indices after management intervention in the simulated fishery.

Based on this modelling, I then applied a series of increasingly complex spatial and temporal generalised linear mixed modelsto investigate their ability to mitigate biases arising from spatial use patterns. I found that the most complex space-time model, which explicitly accounted for temporal patterns within small scale populations, can improve bias, especially when it related to the effect of management intervention, but did not necessarily eliminate it.

Applying these models to the CPUE of fishery management area PAU 7, I found that models accounting for temporal dynamics provided the best fit as assessed by model selection criteria; however, they did not confirm the declining trend observed over the most recent years in models that did not explicitly model temporal dynamics in CPUE. Given repeated voluntary reductions in allowable catch in management area PAU 7 over recent years, and the potential for temporally explicit models to reduce such bias, there is a possibility that rebuilding is taking place in this stock, albeit slowly. These new CPUE indices are now being considered as alternative scenarios in stock assessments.