Using GPS logger data to monitor change in the PAU7 pāua (Haliotis iris) fishery


Neubauer, P., & Abraham, E. (2014). Using GPS logger data to monitor change in the PAU7 pāua (Haliotis iris) fishery. New Zealand Fisheries Assessment Report 2014/31. 29 p.


The pāua (Haliotis iris) stock in New Zealand quota management area PAU 7 was at or near the soft limit of 20% of virgin biomass (B0) at the time of the most recent stock assessment (2011). Biomass was projected to increase under the current Total Allowable Commercial Catch (TACC; 187 t) with a probability of increase only slightly above 50%. A shelving scenario suggested that both the probability of increase and the projected biomass would increase substantially with a shelving of 20% of the current TACC. This shelving level was agreed upon by the pāua fishing industry for the 2013–14 fishing year. Anecdotal reports early in the fishing season, however, suggested a decline in catch rates. Lower catch per unit effort (CPUE), in turn, may reflect a decrease in available biomass that could not only offset the expected benefits from shelving, but also drive the stock further towards extremely low biomass levels.

The pāua data logger initiative is an industry-led and Ministry of Primary Industries (MPI) supported programme to achieve fine scale spatial and temporal monitoring of the fishery. Within this programme, data loggers recording dive positions, depth, and duration are worn by individual pāua divers. Catch information is also recorded on separate boat units. A previous assessment of the dive logger data suggested that the catch and effort metrics have the potential to provide fine-scale information on CPUE. Parameters of the pāua dive operation, including dive times and depths, were found to be strong predictors of catch.

Here, data logger data were used to assess changes in standardised catches between the 2012–13 and 2013–14 fishing years. We applied both qualitative comparisons of temporal and spatial trends in catches as well as modeling of catch and effort data to obtain indications of potential changes between these fishing years.

Temporal patterns in catch histories were broadly similar, however lower catches were reported for most months in the 2013–14 fishing year. The logger data suggested that divers searched smaller areas on average during the 2013–14 season to obtain equivalent catches (i.e., higher catch per unit area).

Models of catch and effort data showed a slight increase in effort-standardised catches for the current (2013–14) fishing year. However, this effect was only significant in a fixed-effects model that estimated an overall year change. When estimating interannual change for individual divers and statistical areas within PAU 7, the pattern was more variable: 50% of divers (7 out of 14 with data for both fishing years) had a higher CPUE in the 2013–14 season, while 6 out of 14 divers had a lower CPUE. Similarly, the year trend in statistical areas was highly variable, but did not show a consistent decline from 2012–13 to 2013–14. We conclude that the combined evidence does not support a decline in CPUE and overall performance of the fishery.