Distributional data on planktic copepods (Crustacea, Copepoda) collected in the framework of the III^rd, V^th, and X^th Expeditions of the Italian National Antarctic Program (PNRA) to the Ross Sea sector from 1987 to 1995 are here provided. Sampling was performed with BIONESS and WP2 nets at 94 sampling stations at depths of 0–1,000 m, with a special focus on the Terra Nova Bay area. Altogether, this dataset comprises 6,027 distributional records, out of which 5,306 were obtained by digitizing original data reports and 721 are based on physical museum vouchers curated by the Italian National Antarctic Museum (MNA, Section of Genoa). The MNA samples include 8,224 individual specimens that were identified to the lowest possible taxonomic level. They belong to four orders, 25 families, 52 genera, and 82 morphological units (out of which 17 could be determined at the genus level only). A variety of environmental data were also recorded at each of the sampling stations, and we report original abundances (ind/m³) to enable future species distribution modelling. From a biogeographic point of view, the distributional data here reported represented new records for the Global Biogeographic Information Facility (GBIF) registry. In particular, 62% of the total number of species are new records for the Ross Sea sector and another 28% new records for the Antarctic region.

Zooplankton is a fundamental group in aquatic ecosystems representing the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations in populations with environmental change, especially reacting to heating and water acidification. Marine copepods account for approx. 70% of the abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study, we have used FAIR-inspired legacy data (dating back to the 1980s) collected in the Ross Sea by the Italian National Antarctic Program at GBIF.org. Together with other open-access GIS data sources and tools, it allows one to generate, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework, we used machine learning with three types of algorithms (TreeNet, RandomForest, and Ensemble) to analyze the presence and absence of copepods in different areas and depth classes as a function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow, for the first time, to map-predict the food chain per depth class in quantitative terms, showing the relative index of occurrence (RIO) in 3Dimensions and identifying the presence of each copepod species analyzed in the Ross Sea, a globally-relevant wilderness area of conservation concern. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting the Antarctic copepod presence, also providing useful data to orient future sampling and the management of wildlife and conservation.

Background: Plankton is the essential ecological category that occupies the lower levels of aquatic trophic networks, representing a good indicator of environmental change. However, most studies deal with distribution of single species or taxa and do not take into account the complex of biological interactions of the real world that rule the ecological processes. Results: This study focused on analyzing Antarctic marine phytoplankton, mesozooplankton, and microzooplankton, examining their biological interactions and co-existences. Field data yielded 1053 biological interaction values, 762 coexistence values, and 15 zero values. Six phytoplankton assemblages and six copepod species were selected based on their abundance and ecological roles. Using 23 environmental descriptors, we modelled the distribution of taxa to accurately represent their occurrences. Sampling was conducted during the 2016–2017 Italian National Antarctic Programme (PNRA) ‘P-ROSE’ project in the East Ross Sea. Machine learning techniques were applied to the occurrence data to generate 48 predictive species distribution maps (SDMs), producing 3D maps for the entire Ross Sea area. These models quantitatively predicted the occurrences of each copepod and phytoplankton assemblage, providing crucial insights into potential variations in biotic and trophic interactions, with significant implications for the management and conservation of Antarctic marine resources. The Receiver Operating Characteristic (ROC) results indicated the highest model efficiency, for Cyanophyta (74%) among phytoplankton assemblages and Paralabidocera antarctica (83%) among copepod communities. The SDMs revealed distinct spatial heterogeneity in the Ross Sea area, with an average Relative Index of Occurrence values of 0.28 (min: 0; max: 0.65) for phytoplankton assemblages and 0.39 (min: 0; max: 0.71) for copepods. Conclusion: The results of this study are essential for a science-based management for one of the world’s most pristine ecosystems and addressing potential climate-induced alterations in species interactions. Our study emphasizes the importance of considering biological interactions in planktonic studies, employing open access and machine learning for measurable and repeatable distribution modelling, and providing crucial ecological insights for informed conservation strategies in the face of environmental change.

Sea ice is a major driver of biological activity in the Southern Ocean. Its cycle of growth and decay determines life history traits; food web interactions; and populations of many small, ice-associated organisms. The regional ocean modelling system (ROMS) for sea ice in the western Ross Sea has highlighted two modes of sea ice duration: fast-melting years when water temperature warms quickly in early spring and sea ice melts out in mid-November, and slow-melting years when water temperature remains below 0 °C and sea ice persists through most of December. Ice-associated and pelagic biota in Terra Nova Bay, Ross Sea, were studied intensively over a 3-week period in November 1997 as part of the PIPEX (Pack-Ice Plankton Experiment) campaign. The sea ice environment in November 1997 exhibited features of a slow-melting year, and the ice cover measured 0.65 m in late November. Phytoplankton abundance and diversity increased in the second half of November, concomitant with warming air and water temperatures, melting sea ice and progressive deepening of a still weak pycnocline. Water column phytoplankton was dominated by planktonic species, both in abundance and diversity, although there was also some input from benthic species. Pelagic zooplankton were typical of a nearshore Antarctic system, with the cyclopoid copepod Oithona similis representing at least 90% of total abundance. There was an increase in numbers coinciding with the period of ice thinning. Conversely, ice-associated species such as the calanoid copepods Stephos longipes and Paralabidocera antarctica decreased over time and were found in low numbers once the water temperatures increased. Stratified sampling under the sea ice, to 20 m, revealed that P. antarctica was mainly found in close association with the under-ice surface, while S. longipes, O. similis, and the calanoid copepod Metridia gerlachei were dispersed more evenly.

Climate change represents one of the major drivers of habitat modification that is affecting a wide variety of environments. In coastal environments, great effort is being put in trying to understand and forecast the possible effects of such processes, and the Sea-Level Rise (SLR) is one of the most investigated phenomena. This paper describes the possible effects of different 2100 sea-level scenarios related to greenhouse gas mitigation policies (Representative Concentration Pathways - RCPs). This work was conducted on a supralittoral habitat situated in Genova (Ligurian Sea), and has covered an eventual change of environmental conditions driven by SLR, which might impact the Culicid Acartomyiamariae, a resident species. The wave run-up stemming from the different RCPs was simulated using the XBeach model, and to infer SLR effects on A. mariae life cycle; the results were coupled with data obtained from field surveys. The model outputs highlighted a variation in the wave run-up oscillations under common wave conditions, which might affect the supralittoral area in terms of water input and hydric balance, and the A. mariae life cycle, which is highly dependent on temperature and salinity.