Fish stock fluctuations are affected by two potentially confounding forces: the removal of individuals by fisheries and climatic variations affecting the productivity of fish populations. Disentangling the relative importance of these forces has thus been a question of primary importance for fisheries management and conservation. Through the analysis of long-term time-series for 27 fish stocks from the Northeast Atlantic, the present study shows that the sign and intensity of the effect of temperature on biomass are dependent on the geographical location: the stocks located at the southernmost and northernmost latitudes of our study displayed stronger associations with temperature than the stocks located in the middle range of latitudes. As a consequence, the investigation of the combined effects of exploitation and the environment revealed that the stocks at the northern/southern boundaries of the spatial extent of the species were more prone to combined effects. The interplay between geographic location, climate and exploitation thus plays a significant role in fish stock productivity, which is generally ignored during assessment, thus affecting management procedures.

As biodiversity plummets due to anthropogenic disturbances, the conservation of oceanic species is made harder by limited knowledge of their distributions and migrations. Indeed, tracking species distributions in the open ocean is particularly challenging due to the scarcity of observations and the complex and variable nature of the ocean system. In this study, we propose a new method that leverages deep learning, specifically convolutional neural networks (CNNs), to capture spatial features of environmental variables. This novelty eliminates the need to predefine these features before modelling and creates opportunities to discover unexpected correlations. Our aim is to present the results of the first trial of this method in the open ocean, discuss limitations and provide feedback for future improvements or adjustments. In this case study, we considered 38 taxa comprising pelagic fishes, elasmobranchs, marine mammals, marine turtles and birds. We trained a model to predict probabilities from the environmental conditions at any specific point in space and time, using species occurrence data from the Global Biodiversity Information Facility (GBIF) and environmental data from various sources. These variables included sea surface temperature, chlorophyll concentration, salinity and fifteen others. During the testing phase, the model was applied to environmental data at locations where species occurrences were recorded. The classifier accurately predicted the observed taxon as the most likely taxon in 69% of cases and included the observed taxon among the top three most likely predictions in 89% of cases. These findings show the adequacy of deep learning for species distribution modelling in the open ocean. Additionally, this purely correlative model was then analysed with explicability tools to understand which variables had an influence on the model’s predictions. While variable importance was species-dependent, we identified finite-size Lyapunov exponents (FSLEs), sea surface temperature, pH and salinity as the most influential variables, in that order. These insights can prove valuable for future species-specific ecology studies.

The Atlantic bluefin tuna (ABFT) is a highly prized species of large pelagic fish. Studies of their environmental physiology may improve understanding and management of their populations, but this is difficult for mature adults because of their large size. Biologging of heart rate holds promise in investigating physiological responses to environmental conditions in free‐swimming fishes but it is very challenging to anesthetize large ABFT for invasive surgery to place a tag in the body cavity near to the heart. We describe a novel method for rapid deployment of a commercially available heart‐rate tag on ABFT, using an atraumatic trocar to implant it in the musculature associated with the cleithrum. We performed three sequential experiments to show that the tagging method (1) is consistently repeatable and reliable, (2) can be used successfully on commercial fishing boats and does not seem to affect fish survival, and (3) is effective for long‐term deployments. In experiment 3, a tag logged heart rate over 80 days on a 60‐kg ABFT held in a farm cage. The logged data showed that heart rate was sensitive to prevailing seasonal temperature and feeding events. At low temperatures, there were clear responses to feeding but these all disappeared above a threshold temperature of 25.5°C. Overall, the results show that our method is simple, rapid, and repeatable, and can be used for long‐term experiments to investigate physiological responses by large ABFT to environmental conditions.

Repeated fish handling may cause stress, which biases experiments and so affects the results. In order to reduce this, the present study investigates the benefit of using bioimpedance analysis to estimate morphological parameters. Bioimpedance analysis is a non-lethal and integrative electrical measurement that can be used to estimate several kinds of physiological parameters and is used in medicine and ecological studies. In particular, bioimpedance can monitor the individual growth of fish, which is a prerequisite for most biological and ecological studies, as body size is one of the best predictors for numerous life history traits and ecological parameters. After a short review on the use of bioimpedance measurement in medicine and ecology, we illustrate the potential of bioimpedance spectroscopy, as opposed to single frequency measurement, for experimental studies on fish. Using a monolithic four-contact electrode and a cost-effective portable system, we conducted bioimpedance spectroscopy on 159 sardines. The association between the length, weight, and electrical parameters obtained at different frequencies from the bioimpedance spectroscopy was investigated. Our results show that accounting for more than one frequency substantially improves the prediction of length and weight. We conclude that bioimpedance could potentially be a powerful tool for monitoring fish growth in ecological studies.