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Diving patterns of air-breathing predators were monitored from three moored subsurface upward-looking echosounders. Complete and partial dive profiles were visible on active acoustic records as echoes that started and/or returned to the surface. Dive metrics: maximum dive depths, durations, and wiggle count were measured and angles, distances, and velocities, were calculated at each site. Dive shapes ‘U’, ‘V’ and ‘W’ were derived using the number of wiggles and the percentage of dive bottom time. Dive profiles were classified into four types with type 1 dives being short in total duration and distance, low velocities, small angles, shallow, and linked to ‘U’ and ‘W’ shapes. Type 2 dives were short in distance, had low velocities, shallow depths, and were linked to ‘V’ dives. Dive types 3 and 4 had higher velocities, larger angles, longer total durations, and were deeper than types 1 and 2. Observed dive types could correspond to travelling, exploring, and foraging predator behaviors. Significant predator-prey overlaps occurred with predator dive profile counts correlated with krill aggregation thickness, density, and depth. This study demonstrates the utility of using stationary active acoustics to identify predator dive profiles with a simultaneous characterization of the potential prey field.

Southern Ocean phytoplankton form the base of the Antarctic food web, influencing higher trophic levels through biomass and community structure. We examined phytoplankton distribution and abundance in the Indian Sector of the Southern Ocean during austral summer as part a multidisciplinary ecosystem survey: Trends in Euphausiids off Mawson, Predators and Oceanography (TEMPO, 2021). Sampling covered six meridional transects from 55-80°E, and from 62°S or 63°S to the ice edge. To determine phytoplankton groups, CHEMTAX analysis was undertaken on pigments measured using HPLC. Diatoms were the dominant component of phytoplankton communities, explaining 56% of variation in chlorophyll a (Chl a), with haptophytes also being a major component. Prior to sampling the sea ice had retreated in a south-westerly direction, leading to shorter ice-free periods in the west (< 44 days, ≤65°E) compared to east (> 44 days, ≥70°E), inducing a strong seasonal effect. The east was nutrient limited, indicated by low-iron forms of haptophytes, and higher silicate:nitrate drawdown ratios (5.1 east vs 4.3 west), pheophytin a (phaeo) concentrations (30.0 vs 18.4 mg m-2) and phaeo:Chl a ratios (1.06 vs 0.53). Biological influences were evident at northern stations between 75-80°E, where krill “super-swarms” and feeding whales were observed. Here, diatoms were depleted from surface waters likely due to krill grazing, as indicated by high phaeo:Chl a ratios (> 0.75), and continued presence of haptophytes, associated with inefficient filtering or selective grazing by krill. Oceanographic influences included deeper mixed layers reducing diatom biomass, and a bloom to the north of the southern Antarctic Circumpolar Current Front in the western survey area thought to be sinking as waters flowed from west to east. Haptophytes were influenced by the Antarctic Slope Front with high-iron forms prevalent to the south only, showing limited iron transfer from coastal waters. Cryptophytes were associated with meltwater, and greens (chlorophytes + prasinophytes) were prevalent below the mixed layer. The interplay of seasonal, biological and oceanographic influences on phytoplankton populations during TEMPO had parallels with processes observed in the BROKE and BROKE-West voyages conducted 25 and 15 years earlier, respectively. Our research consolidates understanding of the krill ecosystem to ensure sustainable management in East Antarctic waters.

The stock assessment model for the Antarctic krill fishery is a population model operating on daily timesteps, which permits modeling within-year patterns of some population dynamics. We explored the effects of including within-year patterns in natural and fishing mortality on catch limits of krill, by incorporating temporal presence of key predator species and contemporary temporal trends of the fishing fleet. We found that inclusion of within-year variation in natural and fishing mortalities increased catch limits. Fishing mortality had a greater effect than natural mortality despite differences in top-down predation on krill, and potentially increased catch limits by 24% compared to the baseline model. Additionally, the stock assessment model allowed a higher catch limit when fishing was during peak summer months than autumn. Number of days with active fishing was negatively related to precautionary catch limits. Future stock assessments should incorporate contemporary spatiotemporal fishing trends and consider implementing additional ecosystem components into the model.

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R2 approximate to 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

Did you know that ecosystems support the wellbeing of humans by simply existing? An ecosystem describes the living things in an area, their interactions, and their environment. The ways that ecosystems benefit the wellbeing of humans are called ecosystem services. There are several types of ecosystem services: supporting (they support animals and their homes), provisioning (they provide food and other materials), cultural (they support our hobbies and cultural activities, such as tourism and arts), or regulating (they regulate our climate, for example by taking up carbon dioxide). Understanding the importance of an ecosystem through its ecosystem services helps guide decisions regarding the environment, such as how much fishing or ship traffic should be allowed in an area, or if an area or species should be protected. In this article, we describe the specific ecosystem services of the sea ice and Southern Ocean around Antarctica.