Short-term socio-ecological effects of a localised change in commercial fishing pressure in Queensland, Australia

Commercial netting closures near three regional cities of Queensland were implemented in 2015 to conserve commercially and recreationally important species by reducing commercial harvest pressure on fish stocks, increase recreational fishing opportunities, marine-based tourism, and resultant economic growth. Understanding the social, ecological, and economic effects of the closures can allow for future adjustments to improve recreational catch and effort factors. The current study compared the values of the three recently established net-free zones (NFZs) with three reference areas of Queensland where commercial net fishing activities continue. For the social aspect, the study determined and compared the recreational fishers’ satisfaction and expectations between a NFZ and a reference site. Recreational fishers were surveyed when returning from fishing tackle stores. Along with the graphical summary of Likert scale responses, non-parametric tests and regression analyses were carried out to analyse fishes’ satisfaction. The underlying relationship among recreational fishers’ satisfaction, overall satisfaction, and expectation was identified by developing a structural equation model for a NFZ and a reference site. The result suggested that fishing satisfaction and expectations are higher in the NFZ than in the reference site. The structural equation modelling (SEM) identified the most influential factors that represent latent variable satisfaction and expectation and demonstrated the relationship and the strength of their relationship for each of the study sites. It is expected that the netting closure might improve the stock structure of the commercially and recreationally important fish barramundi (Lates calcarifer) through natural recruitment. For the ecological aspect, the study developed and tested autoregressive integrated moving average with exogenous input (ARIMAX) models and lagged multiple linear regression (MLR) models to predict and establish the relationship between barramundi catch per unit effort (CPUE) and some fishery and environmental factors that affect barramundi. The study used 30 years of time series data from the secondary sources for the three NFZs and three reference sites. The finding suggests that the ARIMAX model outperformed the MLR model. The study also demonstrated that both fishery and environmental parameters played a role in influencing the CPUE, but most scenarios showed that environmental parameters such as rainfall, streamflow, and stream water level and fishery parameters such as licences and price are the key determinants of CPUE. The study provided valuable insights into the effect of management changes in the commercial CPUE to ensure recreational opportunities and sustainable management of barramundi.