Developing an operating model and testing management procedures for pāua (Haliotis iris) fisheries in PAU 4

Quota management area (QMA) PAU 4 (Chatham Islands) is currently the largest pāua (Haliotis iris) fishery in New Zealand by landings; however, due to concerns over reported catch and effort, no formal integrated stock assessment of the resource has been successfully conducted to date, and the status of the fishery continues to be uncertain. The present project aimed to develop an operating model, and test management procedures that could formalise current pāua statistical-area scale industry management initiatives.

Operating models were developed as spatial length-based models. Due to a lack of sufficiently reliable time series of catch and catch-per-unit-effort (CPUE), stock assessment models could not be fitted statistically, but were conditioned on assumed catch times series. Conditioning assumes a current stock status, and produces a range of stock trajectories that produce assumed status.

Status assumptions were initially derived from a meta-analysis of stock status against CPUE in QMAs with accepted stock assessments. Results from this analysis suggested high status, and did not reflect industry concerns that led to the shelving of annual catch entitlements over the past decade. More conservative assumptions about stock status were, therefore, used to condition models, with conditioning scaled spatially from an analysis of recent spatial CPUE.

Management procedures were developed from a template applied in other pāua fisheries, and centred on a target catch rate provided by fishers. Rules were then scaled according to assumed spatial differences in abundance derived from spatial CPUE. These rules were used as a preliminary set of rules to test the potential of formalising current management practice.

Conditioned models suggested a range of outcomes across statistical areas. Nevertheless, while application of control rules still led to variable outcomes at the statistical-area scale, the spatial variability averaged out across the larger scale. This averaging led to highly stable trends at the QMA scale, and indicated low risk of further declines under the trialled harvest control rules.