The correct prediction of the shape and strength of density dependence in productivity is key to predicting future stock development and providing the best possible long‐term fisheries management advice. Here, we identify unbiased estimators of the relationship between somatic growth, recruitment and density, and apply these to 80 stocks in the Northeast Atlantic. The analyses revealed density‐dependent recruitment in 68% of the stocks. Excluding pelagic stocks exhibiting significant trends in spawning stock biomass, the probability of significant density dependence was even higher at 78%. The relationships demonstrated that at the commonly used biomass limit of 0.2 times maximum spawning stock size, only 32% of the stocks attained three quarters of their maximum recruitment. This leaves 68% of the stocks with less than three quarters of their maximum recruitment at this biomass limit. Significantly lower recruitment at high stock size than at intermediate stock size was seen in 38% of the stocks. Density dependence in late growth occurred in 54% of the stocks, whereas early growth was generally density‐independent. Pelagic stocks were less likely to exhibit density dependence in recruitment than demersal and benthic stocks. We recommend that both the degree to which productivity is related to density and the degree to which the relationship changes over time should be investigated. Both of these aspects should be considered in evaluations of whether sustainability and yield can be improved by including density dependence in forecasts of the effects of different management actions.

Monitoring and understanding the behavior of fishing vessels are important in facilitating effective management, preventing illegal fishing, informing fishing grounds and evaluating effects of harvests on fishery resources. In recent decades, a large quantity of real-time data of fishing vessels have become available with the development of vessel-tracking systems, making it possible to study the behavior of fishing vessels in high spatial and temporal resolutions. To effectively and efficiently deal with the large amount of data, algorithms from artificial intelligence (AI) are increasingly applied in the study of fishing vessel behavior. In this paper, we first introduce the various data sources for studying fishing vessel behavior and compare their pros and cons. Secondly, we review the AI methods that have been used to monitor and extract the behavior of fishing vessels from big data. Then, studies on the physical, ecological and social mechanisms affecting the behavior of fishing vessels were synthesized. Lastly, we review the applications of fishing vessel behavior in fishery science and management.

Anomalous local temperature and extreme events (e.g. heat-waves) can cause rapid change and gradual recovery of local environmental conditions. However, few studies have tested whether species distribution can recover following returning environmental conditions. Here, we tested for change and recovery of the spatial distributions of two flatfish populations, American plaice (Hippoglossoides platessoides) and yellowtail flounder (Limanda ferruginea), in response to consecutive decreasing and increasing water temperature on the Grand Bank off Newfoundland, Canada from 1985 to 2018. Using a Vector Autoregressive Spatiotemporal model, we found the distributions of both species shifted southwards following a period when anomalous cold water covered the northern sections of the Grand Bank. After accounting for density-dependent effects, we observed that yellowtail flounder re-distributed northwards when water temperature returned and exceeded levels recorded before the cold period, while the spatial distribution of American plaice has not recovered. Our study demonstrates nonlinear effects of an environmental factor on species distribution, implying the possibility of irreversible (or hard-to-reverse) changes of species distribution following a rapid change and gradual recovery of environmental conditions.

Oceanic temperature fluctuations are one of the leading factors affecting marine fish populations. Indian Ocean Dipole (IOD), characterized as the sea surface temperature (SST) anomaly change, is an ocean–atmosphere interactive process causing interannual climate variability in the Indian Ocean. Influences of the IOD on the tuna catch rates are supported by previous research. Yet, there remains limited information about the impacts on the abundance of tuna stocks. In this study, we used the standardized Catch Per Unit Effort (CPUE) index to present the stock abundance and compared the effects of the IOD on the bigeye tuna (Thunnus obesus) and yellowfin tuna (Thunnus albacares) among different management areas of the Indian Ocean Tuna Commission (IOTC). Results show significant correlations between IOD events on both species’ abundance in the tropical western Indian Ocean. However, in the tropical eastern Indian Ocean and the southern Indian Ocean, neither bigeye nor yellowfin tuna abundances were significantly correlated by the IOD. For the whole Indian Ocean, IOD was significantly correlated uniquely with the yellowfin tuna abundance. Our results emphasized the importance of evaluating the climate variability effects over fisheries abundance species by species and per fishing areas analyses.

Effective fisheries management is the key to achieve sustainable fisheries globally, while accurate monitoring of fishing vessels is essential to improve the effectiveness of management measures. Self-reported information on vessel types is often limited and may not cover all operating fishing vessels, causing incomplete monitoring in fisheries management. Therefore, a novel way to objectively identify the types of a large quantity of fishing vessels is needed. In this study, we presented an innovative integrated deep learning model by using automatic identification system (AIS) data to classify five types of fishing vessels, including gillnetter, hook and liner, trawler, fish carrier, and stow net vessel, further improving the performance of fishing vessel classification. First, we preprocessed data by removing erroneous information, dividing the vessel trajectories by day to obtain a complete and reliable dataset. Then, a multidimensional feature vector was constructed by combining the geometric, static and dynamic characteristics of fishing vessels to explain the behavioral differences of various types of fishing vessels more effectively. Finally, the feature vector was fed into an ensemble model of a two-dimensional bidirectional long short-term memory network and a convolutional neural network with an attention mechanism for training, and the prediction results were obtained through a fully connected layer. The accuracy of the ensemble model was 91.90%, which was higher than other single classifiers. The experimental results demonstrated that this method obtained remarkable performance and could be adopted to improve the precision of fishing vessel classification based on AIS data.