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Temporal distributions of Antarctic krill (Euphausia superba) density and aggregation types were characterized and compared using Nortek Signature100 and SIMRAD Wideband Autonomous Transceiver (WBAT) upward-looking echosounders. Noise varied between the two echosounders. With the Signature100, it was necessary to correct data for background, transient, and impulse noises, while the WBAT data needed to be corrected for background noise only. For selected regions with no visible backscatter, the signal-to-noise ratio of Sv values (i.e. the ratio between the signal and the background noise level) did not vary between the two echosounders. Surface echo backscatter was similar during similar time periods. Descriptive metrics were used to quantify spatial and temporal krill vertical distributions: volume backscatter, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. Krill backscatter density differed between the two instruments but was detected at similar mean depths. Krill aggregations were identified at each mooring location and classified in three types based on morphological characteristics. Each type of aggregation shape differed at the two spatially separated moorings, while the acoustic density of each aggregation type was similar. The Signature100 detected a lower number of krill aggregations (n = 133) compared to the WBAT (n = 707). Although both instruments can be used for autonomous deployment and sampling of krill over extended periods, there is a strong caveat for the use of the Signature100 due to significant differences in noise characteristics and krill detection.

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.

Krillscan software was developed to automatically process echosounder data and achieve an accelerated and transparent analysis of backscatter data that allows calculation of target biomass. Herein, the fishery for Antarctic krill (Euphausia superba, Henceforth Krill) was used as a case study to develop the approach. Implementation of a sustainable management strategy for the krill fishery is complicated by a lack of regularly updated krill abundance data on spatiotemporal scales of the fishery. To increase krill biomass data availability, automatic echosounder data processing and swarm detection software was tested against traditional manual scrutinization with LSSS software and agreed with only minor offsets in estimated nautical area scattering coefficients. In addition to automatic processing and data transfer, Krillscan also has a graphical user interface to supervise automatic krill swarm detection. Echogram size can be compressed up to 100 times and raw data are processed faster than generated, thereby enabling near-real time analysis and data transfer. Compressed data can be transmitted online to allow fishing vessels to conduct surveys without having scientific personnel with special expertise on board.

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.