No abstract

We assessed the potential for simulation and modelling of the blackspot seabream (Pagellus bogaraveo) population in the Strait of Gibraltar to discriminate the environmental effects of fishery impacts. A discrete biomass–abundance dynamic model was implemented to obtain a simulated monthly time series of blackspot seabream biomass. On this simulated time series, autoregressive integrated moving average (ARIMA) models were fitted. The best ARIMA fit provided a significant correlation of 0.76 and persistence index higher than 0.85. The proportion of variance non‐explained by the ARIMA models was correlated with a time series of sea surface temperature (SST) and North Atlantic Oscillation (NAO). The analysis of global, annual and winter correlation between the proportion of variance not explained by the ARIMA models and environmental variables showed that significant associations were not detected over the full time series. Our analysis therefore suggests that overexploitation is the main factor responsible for the commercial depletion of blackspot seabream in the Strait of Gibraltar.

Tuna species were one of the four most valuable fishing classes (together to lobsters, shrimps, and cephalopods) in 2014 (FAO, 2016), and they are considered an important contributor to global food security (Báez, Pascual-Alayón, Ramos, & Abascal, 2018). During 2014, the total catches of tuna and tuna-like species reached a new record, with almost 7.7 million tonnes caught and delivered to market (FAO, 2016). The yellowfin tuna (Thunnus albacares) (YFT) is among the eight marine species with the highest catches globally (FAO, 2016). The Spanish purse seine freezer fleet operating in the Indian Ocean is of the fleets with most YFT catches globally. It consists of a total of 15 fishing boats supported by 6 non-fishing vessels, mainly managing the floating objects stock (deployment, detection, tuna school estimation, etc.). During 2014, the Spanish purse seiners from Indian Ocean caught 3.95% of the yellowfin tuna tonnes landed worldwide (data deducted from Báez et al., 2017). The "Instituto Español de Oceanografía" (IEO) in Spain is responsible for producing scientific estimates of catch, effort, and other biological data for the Spanish purse seine fleet. Since 1990, the annual catch by species from Spanish purse seine freezer fleet operating in the Indian Ocean has been reported to the Indian Ocean Tuna Commission (IOTC) (see Báez et al., 2018 for the latest available report).

Species and size selectivity of the deep water longline traditionally used in commercial fishing of the black spot seabream (Pagellus bogaraveo) were studied in the Strait of Gibraltar with four sizes of hooks. Black spot seabream contributed up to 88% of the catch by number. Catch and by-catch rates differed for the different hooks and fishing trials. Significant differences in average fish length between all hooks, except in one case, were found. The comparison of two experimental fishing trials within 4 years indicates a displacement towards smaller sizes in the size frequency distributions. The results of this study show that the fishing gear can be size selective depending on hook size. The fitted selectivity models for each experiments were very different despite having two hooks in common. This is probably due to the very different catch size distributions in the two periods, which suggests that the population size structure changed significantly between

a b s t r a c tSize selectivity of the deep water longline used in the black spot seabream (Pagellus bogaraveo) fishery in the Strait of Gibraltar was studied with data of four sizes of hooks. Logistic (classic) and Artificial Neural Networks (heuristic) selectivity models were fitted for two experimental fishing trials. Logistic selectivity model was adequate for only one of the two periods analysed and the inferior results obtained with the classical approach were significantly improved by ANNs. These results indicate that in the event that the classic models do not fit well, perhaps due to poor quality of the data (such as a smaller sample size or highly overlapped distributions), the simpler ANNs models, with capacity to combine linear relationships and highly non-linear, are most appropriate to establish the functional relation between variables.