To identify the dietary composition and characteristics of both Adélie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins at four breeding sites, we performed stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotope analysis of down samples taken from penguin chicks. Adélie Penguin chicks at Cape Hallett mostly fed on Antarctic krill (Euphausia superba; 65.5 ± 3.5%), a reflection of the prevalence of that species near Cape Hallett, and no significant differences were noted between 2017 and 2018. However, Adélie Penguin chicks at Inexpressible Island, located near Terra Nova Bay, fed on both Antarctic silverfish (Pleuragramma antarctica; 42.5%) and ice krill (Euphausia crystallorophias; 47%), reflecting the high biomass observed in Terra Nova Bay. Meanwhile, no significant difference was noted between the two breeding sites of the Emperor Penguin. Emperor Penguin chicks predominantly fed on Antarctic silverfish (74.5 ± 2.1%) at both breeding sites (Cape Washington and Coulman Island), suggesting that diet preference represents the main factor influencing Emperor Penguin foraging. In contrast, the diet of the Adélie Penguin reflects presumed regional differences in prey prevalence, as inferred from available survey data.

Diel vertical migration (DVM), which refers to the global daily migration of zooplankton and micronekton (hereafter ‘zooplankton’), serves an important function in oceanic ecosystems. DVM can be recognized as a variation in the sound scattering layer (SSL), but no standard exists for identifying SSLs, and no definition has been proposed to describe the basic features of DVM. Hence, a standardized DVM detection method is needed to report consistent results and efficiently compare the parameters over broad ocean areas and long time‐scales. We developed an automated, quantitative method for detecting DVM and identifying the characteristic parameters using 2D cross‐correlation. We established the DVM trajectory model with linear and sinusoidal parts featuring a specific height and width. The 2D cross‐correlation method was applied to acoustic echogram images of the volume backscattering strength (Sv, dB re 1 m⁻¹) with a synthetic image made from the DVM trajectory model. From the cross‐correlation coefficients, we found the candidate lines exhibiting the strongest possibility of being DVM trajectories. We tested the DVM detection method on the acoustic echograms for 273 days, of which Sv was gathered by a bottom‐moored, upward‐looking acoustic Doppler current profiler (ADCP). For each identified DVM trajectory, four quantitative parameters describing their spatial and temporal structures (the maximum and minimum depths and ascent and descent times) were determined, and the existence of multi‐layer was recognized. We confirmed that this method showed better performance than the prior method based on the weighted mean depth (WMD), and as a result of visual scrutiny inspection, our method showed 88% DVM detection performance. Our goal was to suggest a robust, quantitative, automated DVM detection method using 2D cross‐correlation. The method was successfully applied to detect DVM trajectories and define the characteristic parameters regardless of fluctuation or the multi‐layer structure due to its robustness and simple model. Analysing DVM behaviours across environments with this method could help reveal the significance of these behaviours in ocean environments.

Meso- and macrozooplankton play crucial roles in the trophic web and the biological carbon pump in the ocean by transferring energy from lower to higher trophic levels and vertically exporting carbon from the surface to the deep ocean and seabed. In this study, zooplankton community structures in the Ross Sea, Antarctica, were analyzed using metabarcoding methods. Both regular barcode (RB) (using a PacBio Sequel system) and mini barcode (MB) (using the Illumina MiSeq platform) methods were utilized. As the result of a combination of the two bioinformatic pipelines used in the RB, 55 reliable haplotypes were obtained from the pooled zooplankton net samples, whereas 183 amplicon sequence variants (ASVs) were isolated from the MB metabarcoding analyses of 14 individual stations. Among these, 39 (70.9%) and 125 (90.6%) showed higher than 99% sequence identity to the database, indicating that there were sufficient reference sequences to employ metabarcoding analysis—except for several taxa, including small-sized copepods, cnidarians, and pneumodermatids. A high degree of shared taxa showed that both metabarcoding analyses were feasible for use in the analysis of zooplankton assemblages in the Ross Sea. However, RB would be more useful for the construction of a reference database due to its relatively high cost, whereas MB would be more economic for ecological surveys due to its relatively low cost (albeit, only if reference sequences were well documented using RB). Zooplankton assemblages were highly diverse in each sample site, presumably due to the narrow covered volumes of the vertical net-towed samples from polynyas in the Ross Sea. As metabarcoding data accumulate, we will gain better insights into zooplankton communities and their ecological implications in the Ross Sea.

Antarctic krill (Euphausia superba) and ice krill (Euphausia crystallorophias) are vital to the Southern Ocean ecosystem, yet their swarming behavior remains poorly understood. This study applied a novel approach in the Ross Sea by directly linking acoustically detected swarms with net-captured krill, enabling precise identification of species and body size-key traits often unavailable in previous studies. From 14 Antarctic krill and 55 ice krill swarms, key properties-depth, length, height, area, and packing concentration-were extracted. Hierarchical clustering grouped swarms into two Antarctic krill groups and three ice krill groups with significant differences (P < 0.01). Antarctic krill swarms were often shallower, in warmer, less saline waters away from the coastline and ice-edge, compared to ice krill. Larger Antarctic krill (47.5 mm) formed swarms about 40 times smaller in area than smaller ones (40.1 mm). The smallest ice krill (11.4 mm) appeared in Terra Nova Bay, at greater depths, forming dense swarms. Despite similar body sizes and environmental conditions, some ice krill swarms were up to 15 times larger than others. Body length was a key factor influencing swarm properties. These findings provide new insights into swarming strategies and behavioral ecology of both species in the Ross Sea.

The Ross Sea region Marine Protected Area (RSR MPA) is one of the most productive regions in the Southern Ocean. Mesozooplankton intermediates the primary product to the higher predators, such as penguins and seals, in this ecosystem. In this study, the mesozooplankton community structure and spatial pattern in the RSR MPA in January were investigated by using 505 μm-mesh-size bongo net samples. As a result, 37 mesozooplankton taxa with a total mean abundance of 35.26 ind./m³, ranging from 2.94 to 139.17 ind./m³, were confirmed. Of the 37 taxa, 7 occupied almost 84% of the total abundance, with copepods being the main dominant taxa. As shown by our hierarchical analysis, the mesozooplankton community was divided into four groups, each associated with a specific geographical distribution. Group A was composed of stations around Terra Nova Bay and showed relatively low abundance. Group B included stations around the continental slope region. Group D was composed of the Ross Sea continental shelf stations, while group C consisted of stations geographically located between those of groups B and D. These four groups were influenced by various environmental factors, such as water temperature, salinity, and nutrients. In summary, the mesozooplankton community can be separated according to geographical pattern. This pattern is related to several environmental factors.

Antarctic silverfish (Pleuragramma antarcticum) play a crucial intermediary role in connecting top predators and krill in the food web of the Antarctic Ocean. Despite their crucial role, research on their abundance is lacking. In this study, we estimated the abundance of juvenile Antarctic silverfish as foundational data for predicting their abundance. The density of juvenile Antarctic silverfish was estimated using an acoustic backscattering theoretical model. The mean volume backscattering strength was used to investigate the vertical and horizontal distributions of juvenile Antarctic silverfish in the Antarctic Ross Sea. The survey area was located near Cape Hallett, Antarctica, where Antarctic krill (Euphausia superba), ice krill (E. crystallorophias), and Antarctic silverfish coexist. The survey was performed four times using the Korean Antarctic research ship, RV Araon (R/V, 7507 GT). Frame trawls were conducted to identify the length and weight of the target fish species in the survey area. Captured Antarctic silverfish captured measured 3–9 cm. The maximum target strength (TS) was −92.93 dB at 38 kHz, −86.63 dB at 120 kHz, and 85.89 dB at 200 kHz. The average TS was −100.00 dB at 38 kHz, −93.00 dB at 120 kHz, and −106.90 dB at 200 kHz. Most juvenile Antarctic silverfish were found at a depth of 100 m and were distributed closer to sea ice. Between nearshore and polynya waters, the fish demonstrated a proclivity for polynya waters.