Bycatch is a significant issue in global fisheries and understanding the length–weight relationships (LWR) of fish species can provide valuable insights for stock assessment and management efforts. In this study, we estimated the LWR of 74 fish species in trawl fleet discards from the Gulf of Cadiz, including 24 species for which LWR data had not been previously reported in this region. LWR was calculated from the formula W = aL^b where parameter a is the intercept of the equation, related to body shape, and parameter b is the slope, which indicates the type of growth of the species. A total of 20,007 individuals from 40 families were measured and weighed. The most abundant species were Engraulis encrasicolus, Trachurus trachurus, Serranus hepatus, Sardina pilchardus, Capros aper, and Diplodus bellottii, and the Sparidae family was the most represented with ten species. The parameter b, which represents the type of growth, ranged from 2.1607 to 3.7040. A positive allometric growth trend was observed in 64% of the species. The inclusion of individuals with a low sample size proved useful, particularly for first reports in a new study area. However, caution should be taken when using these data, as the estimates of the length–weight relationship for these species may be less precise. Further studies with larger sample sizes are needed to confirm the results and improve the accuracy of the estimates. Overall, our findings contribute to the understanding of the LWR of fish species in the Gulf of Cadiz, informing future research and management efforts in the region.

Several studies have shown the effect of climatic oscillations on fisheries. Small pelagic fish are of special global economic importance and very sensitive to fluctuations in the physical environment in which they live. The main goal of this study was to explore the relationship between the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), and the Arctic Oscillation (AO) on the landings and first sale prices of the most representative small pelagic commercial species of the purse-seine fisheries in the Gulf of Cadiz (North East Atlantic), the European anchovy Engraulis encrasicolus and the European sardine Sardine pilchardus. Generalised linear models (GLMs) with different data transformations and distribution errors were generated to analyse these relationships. The best results of the models were obtained by applying a moving average of order 3 to the dataset with a double weighted median. Our results demonstrate relationships between NAO, AO, and EA and European anchovy and sardine landings. These cause an indirect effect on the first sale price in markets through catch variations, which affect the price according to the law of supply and demand. The limitations of this study and management implications are discussed.

Otoliths are widely employed in marine sciences to gain insights into fish growth, age, migrations, and population structure. This study investigates the relationships between morphometric measurements, otolith characteristics, and length size patterns in the brown comber (Serranus hepatus) from the Gulf of Cádiz, a species discarded in artisanal trawl fisheries. Our findings reveal significant changes in otolith shape indices as fish grow, with symmetry observed between left and right otolith measurements. Otolith size is found to be related to fish size, supporting its use in estimating body length at different life stages. Otolith shape analysis has potential applications in stock identification, detecting catch misreporting, and studying marine predator diets. Combining otolith shape analysis with other data types can clarify relationships among taxa and inform spatial management strategies, contributing to the long‐term sustainability of fish populations and the assessment of the impact of management strategies on fish size and growth. This study enhances our understanding of the broader implications of morphometric and otolith analyses in fisheries research and supports the development of more sustainable fisheries management practices.

Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi‐layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non‐linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health.

In the Northern Alboran Sea, artisanal small-scale fisheries using trammel nets suffer economic losses, and local fishermen see their way-of-life endangered, due to interactions with wildlife species such as alien species and dolphins. On the one hand, the alien seaweed Rugulopteryx okamurae, which was first recorded in the Alboran Sea in 2015, has undergone an intensive expansion in the sub-region, monopolizing the available seabed, causing radical changes in the underwater seascape and clogging the trammel nets. On the other hand, the damage caused to the fishing nets by dolphin fish predation is an ancient problem worldwide, but it is intensifying in the last years. The main objective of this study is to understand the main environmental and technical conditions that favor damages of fishing trammel nets in the Alboran Sea, which entails an important loss of catchability, due to (i) the clogging of the artisanal fishing trammel nets by invasive seaweed, and (ii) the breaking of the nets by dolphin predation. Through close monitoring of fishermen in port, we obtained direct information of 548 sets. Our results indicate that approximately 30% of trammel sets suffered a damage due to unwanted interaction with alien seaweeds and dolphins. As seaweeds invasion is a global problem while dolphin-fishing gear interaction is more local, we concluded that only a large-scale management of exotic algae, together with the involvement of local fishermen, could solve the economic problems of this activity.

The accurate labeling of species and size of specimens plays a pivotal role in fish auctions conducted at fishing ports. These labels, among other relevant information, serve as determinants of the objectivity of the auction preparation process, underscoring the indispensable nature of a reliable labeling system. Historically, this task has relied on manual processes, rendering it vulnerable to subjective interpretations by the involved personnel, therefore compromising the value of the merchandise. Consequently, the digitization and implementation of an automated labeling system are proposed as a viable solution to this ongoing challenge. This study presents an automatic system for labeling species and size, leveraging pre-trained convolutional neural networks. Specifically, the performance of VGG16, EfficientNetV2L, Xception, and ResNet152V2 networks is thoroughly examined, incorporating data augmentation techniques and fine-tuning strategies. The experimental findings demonstrate that for species classification, the EfficientNetV2L network excels as the most proficient model, achieving an average F-Score of 0.932 in its automatic mode and an average F-Score of 0.976 in its semi-automatic mode. Concerning size classification, a semi-automatic model is introduced, where the Xception network emerges as the superior model, achieving an average F-Score of 0.949.