Developing an operating model and testing management procedures for pāua (Haliotis iris) fisheries in PAU 3B

Pāua (Haliotis iris) quota management area (QMA) PAU 3B was established in 2021 as the southern area of the former QMA PAU~3, which had the northern area closed between 2017 and 2021, following the 2016 Kaikōura earthquakes. The southern area remained open for p=aua fisheries, albeit at a Total Allowable Commercial Catch (TACC) of half the original TACC of QMA PAU 3 (46~t).

Although there was an existing stock assessment for PAU~3 prior to the earthquakes, the assessment was considered to be poorly representative of southern areas, which were fished considerably less frequently than the Kaikōura area (now PAU 3A). In addition, no representative growth data were available from the area, leading to marked uncertainties about stock status. With the establishment of PAU 3B, there has been increased interest in understanding the stock status of the area, and to develop management measures that can maintain the fishery at target levels. The present project aimed to develop models to understand stock status, and to test potential management procedures in PAU 3B.

For this study, catch, catch-per-unit-effort (CPUE), and length-frequency information were compiled to inform models for stock in PAU 3B. Catch and CPUE information is only known with some certainty since the early 2000s and the establishment of fine-scale pāua statistical areas, which allow partitioning of PAU 3 catches and catch-effort data into PAU 3A and PAU 3B components. Assumptions about spatial catch splits needed to be made to reconstruct catches prior to 2002. Nevertheless, early catches were likely relatively low as the area was less targeted by commercial fisheries than the northern area of PAU 3. The CPUE has remained relatively constant through the 2000s, with a small increase in recent years.

An initial attempt to fit stock assessment models was unsuccessful based on the flat or increasing CPUE, which occurred in the context of increasing catch over time. In the absence of a robust stock assessment model, we explored the use of CPUE (kg/h) relative to CPUE in other areas as a measure of potential stock status or exploitation rate. This approach suggested a relatively low exploitation rate and high stock status near 60% of unfished biomass.

To test potential harvest control rules, we used empirical estimates of stock status to condition operating models using depletion-based stock reduction analysis. The operating models produced a range of outcomes depending on productivity assumptions and conditioning constrains, and were used to test the suitability of control rules to maintain target catch rates.