Development and application of a spatial stock assessment model for pāua (Haliotis iris)

A spatial assessment model for pāua (Haliotis iris) was developed to better incorporate the effect of demographic variability and spatial catch patterns on pāua population dynamics, and to facilitate spatial management procedure evaluation and implementation. The model was fitted to spatially-resolved input data and compared with single-area versions of the assessment models for pāua quota management areas PAU 5B and PAU 5D. These quota management areas (QMAs), chosen as the respective assessment models, allowed a robust comparison based on different characteristics: the model for PAU 5B provides subjectively “good” estimates of model parameters and population trajectories and, therefore, provides a “best case” test, whereas the model for PAU 5D is sensitive to growth assumptions and model weighting, providing a more challenging test case.

The spatial model developed here provided qualitatively different inferences in each QMA compared with the single-area models. The spatial model performed well in technical terms for both QMAs in that it provided well-defined estimates for all model parameters. Nevertheless, it performed markedly differently to the single-area model in PAU 5D, but provided similar estimates to the single-area model in PAU 5B. In PAU 5D, the inferred catch history on the regional scale suggested that only a single area was affected by catch reductions in the mid-2000s, but all areas showed comparable increases in catch-per- unit-effort (CPUE) at this time. The spatial model did not attribute the increase in CPUE (and by extension, available biomass in the model) to the decreases in catch, but estimated a considerably higher biomass, and attributed the increase in CPUE (and available biomass in the model) to recruitment. As a result, the total biomass and stock status in the spatial model were estimated to be markedly higher than in the single-area model.

The spatial model provides an opportunity to incorporate spatial patterns in both fishing and demographics in the assessment and management of pāua. It provides complementary information to inferences made using the single-area model, and can be used to test the impact of spatial homogeneity assumptions in the single-area model.