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Atlantic bluefin tuna (ABFT) stocks have been considered overfished over the last decades, especially the western stock, whose main spawning grounds are in the Gulf of Mexico (GoM). Despite the current measures implemented, spawner bycatch by the longline fleet targeting yellowfin tuna (YFT) may explain the lack of recovery of local stocks. This situation demands the implementation of appropriate spatiotemporal management strategies to minimize bluefin bycatch in the GoM, which involves knowledge in depth of its distribution and environmental forcing. Using catch and effort data from the Mexican commercial longline fleet with 100% scientific observer coverage from 1999 to 2012 and satellite derived environmental data, this study investigated the influence of environmental conditions on catch per unit effort (CPUE) of ABFT and YFT. General additive models (GAMs) were fitted using a negative binomial distribution and applying Akaike information criterion (AIC) to select the best model. Bluefin CPUE exhibited a marked seasonality, reaching higher values in February and March while YFT catches occurred throughout the year. Two main locations were identified with higher ABFT bycatch rates, Campeche Bay and the western‐central area of the GoM. Higher ABFT CPUE was significantly associated with areas with negative sea level anomalies and low sea surface temperatures, characteristic of cyclonic eddies. Instead, YFT CPUE showed a lesser environmental influence in its distribution. To our knowledge, the patterns shown in this study provide the first in‐depth approach to understand ABFT bycatch in Mexican waters, which will help in further development of adequate management strategies.

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Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring polyps activity, used as a proxy of gorgonian feeding activity of three colonies of Placogorgia sp. Images cover a period of 22 days with a temporal resolution of 30 min. In addition, this seafloor observatory is instrumented with oceanographic sensors that allows continuous monitoring of the hydrographic conditions in the site. Deep-learning is used for automatic detection of the state of the polyps registered in the images. More than 1000 images of 3 large specimens of gorgonians are analyzed, annotating polyps as extended or retracted, using the semantic segmentation algorithm ConvNeXt. The segmentation results are used to describe the feeding patterns of this species. Placogorgia sp. shows a daily pattern of feeding conduct, depending on the hours of day and night. Using a Singular Spectrum Analysis approach, feeding activity is related to currents dynamics and Acoustic Doppler Current Profile (ADCP) return signal intensity, as proxy of suspended matter, achieving a linear correlation of 0.35 and 0.11 respectively. This is the first time that the behavior of the Placogorgia polyps, directly related to their feeding process, is described.