Monitoring coastal marine wildlife is crucial for biodiversity conservation, environmental management, and sustainable utilization of tourism-related natural assets. Conducting in situ censuses and population studies in extensive and remote marine habitats often faces logistical constraints, necessitating the adoption of advanced technologies to enhance the efficiency and accuracy of monitoring efforts. This study investigates the utilization of aerial imagery and deep learning methodologies for the automated detection, classification, and enumeration of marine-coastal species. A comprehensive dataset of high-resolution images, captured by drones and aircrafts over southern elephant seal (Mirounga leonina) and South American sea lion (Otaria flavescens) colonies in the Valdés Peninsula, Patagonia, Argentina, was curated and annotated. Using this annotated dataset, a deep learning framework was developed and trained to identify and classify individual animals. The resulting model may help produce automated, accurate population metrics that support the analysis of ecological dynamics. The resulting model achieved F1 scores of between 0.7 and 0.9, depending on the type of individual. Among its contributions, this methodology provided essential insights into the impacts of emergent threats, such as the outbreak of the highly pathogenic avian influenza virus H5N1 during the 2023 austral spring season, which caused significant mortality in these species.

The dataset contains 2007 records of occurrence of 39 species of fish and 2 species of squid distributed on the Patagonian continental shelf and slope. This dataset describes a new and revised version of the original data published through OBIS with individual morphometrics. Specimens are representative of pelagic, demersal, demersal-pelagic, demersal-benthic and benthic habits and they were collected by commercial fishing vessels in autumn (May–June, 2001, 51 catches), winter (July–August, 2001, 38 catches) and summer (January-February, 2002, 112 catches). The sampling was carried out with bottom trawls at a depth range of 73–370 m. The survey was located between 39°–52°S and 55°–65°W.

In this paper we present SES Dashboard, a linked data platform developed to manage information of Southern Elephant Seals (Mirounga leonina Linnaeus, 1758) from Península Valdés colony (Patagonia, Argentina), collected over more than two decades of research. Southern elephant seals spend most of their time foraging at sea, alternating with two short and highly synchronized haul-outs ashore for breeding and molting. On land, the distribution of the colony extends along ≈350 km, while at sea individuals travel thousands of kilometers The information recorded is mainly based on georeferenced data from censuses, bioregisters (trajectories at sea and hydrographic profiles), and bibliography based on the above data. SES Dashboard was released in 2021 and published following the principles of Linked Open Data to improve its visibility, to make it shareable, extensible and easily reusable through a SPARQL endpoint.