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ABSTRACT Understanding diet composition is essential for unravelling trophic interactions in aquatic ecosystems. DNA metabarcoding, utilising various variable regions of the 18S rRNA gene, is increasingly employed to investigate zooplankton diet composition. However, accurate results depend on rapid inactivation of digestive enzymes and DNA nucleases through proper sample processing and preservation. In this study, we compare the prey communities of Antarctic krill retrieved from the 18S variable regions V4 and V7 and assess how different processing treatments affect the detected prey composition of both krill and salps. Our findings highlight the critical importance of prompt sample processing for species with highly efficient digestive enzymes, such as krill, to preserve rapidly digested prey, including gelatinous plankton. Comparative analyses of the V4 and V7 regions revealed significantly different prey communities within the same krill samples, indicating that these regions may not be suitable for direct comparisons within or across studies. To complement molecular approaches, we also analyse fatty acids (FA) as trophic markers which provide insights into dietary habits over both short and long time scales. By comparing FA signals from stomach and tissue samples of the same krill and salp individuals, we identified significant differences in trophic markers representing different plankton groups. These findings emphasise the necessity of separating digestive tract from tissue to distinguish between short- and long-term diet signals. Furthermore, integrating FA analysis with metabarcoding offers valuable insights into zooplankton digestion efficiency across taxonomic levels. This combined approach enhances our understanding of zooplankton feeding ecology and trophic interactions in marine ecosystems.

Understanding how marine predators structure and adjust their foraging in response to prey field characteristics is a longstanding objective in marine ecology. This is particularly challenging in Southern Ocean ecosystems, where logistical and financial constraints hinder assessment of predator foraging and prey field information at relevant spatial and temporal scales. Here, we examine how Adélie penguins, Pygoscelis adeliae, a key Southern Ocean indicator species, perform and organize their foraging behaviour during two contrasting years of krill (Euphausia superba) abundance. Using multiyear krill acoustic data from King George Island in the West Antarctic Peninsula (WAP), we assess broad seasonal conditions in krill availability. We also analyse a suite of penguin biologging data (spatial location, dive and accelerometry-derived activities) during the same period to identify broad behavioural differences in their bout-diving activity, a classical measure of the temporal organization of foraging in diving predators. During years of high krill abundance and availability, penguins performed shorter dive bouts (consisting of shallower and shorter-duration dives), which were more concentrated in time and space. Despite these differences in bout structure, prey capture attempts occurred at the same rate within bouts. These findings challenge traditional interpretations assuming that increased bout durations (and related proxies of prey capture effort) signal increased krill patch abundance and profitability. Although additional data are required to understand the full scope of penguin bout diving and krill prey field associations, our work improves understanding of penguin behavioural variation and provides insights into how foraging behaviours could potentially be used to interpret krill availability at predator- and management-relevant scales.

Euphausia superba is a well-known Antarctic crustacean of great economic and ecological importance, whose management requires accurate and precise abundance and distribution estimates. Such estimates are difficult to achieve given the remoteness, extension, and large spatio-temporal variability of its geographic distribution. Acoustic data collected on board krill fishing vessels during normal fishing operation has a great potential to enhance such abundance and distribution estimates. In the present work we test the hypothesis that design-free hydroacoustic data collected during regular fishing operations can be used to produce abundance and distribution estimates with similar accuracy and precision than design-based scientific surveys. Thus, we produced and compared distribution and abundance estimates produced using either design-free hydroacoustic data collected during regular fishing operations or design-based data from scientific surveys conducted off the South Orkney Islands during summer 2017 and 2019. Following a Bayesian geostatistical approach that considered and fitted simultaneously the spatial and temporal correlation of the data, we tested different auto-correlation structures and selected the most informative models. The comparison included the means and coefficients of variation (CV) of the probability of presence (p), conditional density (d) and relative abundance index (RAI) estimates. In addition, we also simulated scenarios of parallel and orthogonal transects and obtained RAI estimates from each scenario to compare with design-based and design-free estimates for each year. In 2017, the mean RAI estimated using design-free data (94 421 m2; CV: 14 %) was ∼ 50 % higher than the one estimated with design-based data (60 232 m2; CV: 42 %), both within the fishing area. In 2019, the mean RAI estimated using design-free data (509 413 m2 CV: 6 %) was ∼ 5-fold higher than the one obtained using design-based data (113 654 m2; CV: 33 %) in the same area. Design-free RAI estimates were highly sensitive to extrapolating the inference area from fishing to the high-density sub-area. On the other hand, changing from an hourly-resolved spatio-temporal model to a purely spatial model resulted in neglectable changes. Despite observed differences in mean estimates, both methods identified similar areas of high presence and density of Antarctic krill north and north-west of the South Orkney Islands. The 2017 estimate from design-free data was probably affected by a larger dispersion of krill, and a less observed effective area during regular fishing operations. Our results show that despite using state-of-the-art methods for processing and analyzing design-free, acoustic data collected by the fishing fleet, it still yielded unreliable RAI estimates. The bias and uncertainty related to design-free data were reduced when parallel or orthogonal transects were applied although orthogonal transects yielded results with increased accuracy as they were only 21 % lower and 0.02 % higher than the true value in 2017 and 2019, respectively. Other possible approach to minimize bias would be integrating hydroacoustic information from multiple vessels.

The management strategy for the Antarctic krill (Euphausia superba) fishery is being revised. A key aim is to spatially and temporally allocate catches in a manner that minimizes impacts to both the krill stock and dependent predators. This process requires spatial information on the distribution and abundance of krill, yet gaps exist for an important fishing area surrounding the South Orkney Islands in the south Scotia Sea. To fill this need, we create a dynamic distribution model for krill in this region. We used data from a spatially and temporally consistent acoustic survey (2011-2020) and year-specific environmental covariates within a two-part hurdle model. The model successfully captured observed spatial and temporal patterns in krill density. The covariates found to be most important included distance from shelf break, distance from summer sea ice extent, and salinity. The northern and eastern shelf edges of the South Orkney Islands were areas of consistently high krill density and displayed strong spatial overlap between intense fishing activity and foraging chinstrap penguins. High mean krill density was also linked to oceanographic features located within the Weddell Sea. Our data suggest that years in which these features were closer to the South Orkney shelf were also years of positive Southern Annular Mode and higher observed krill densities. Our findings highlight existing fishery?predator?prey overlap in the region and support the hypothesis that Weddell Sea oceanography may play a role in transporting krill into this region. These results will feed into the next phase of krill fisheries management assessment.

Understanding the connection between maturity stages and morphology in relation to size selectivity in trawls is essential for assessing the impact of various fishing gear on the population structures of harvested species, their fishing mortality rates, and the efficiency of the gear used. The Antarctic krill (Euphausia superba) fishery is the largest in the Southern Ocean by volume, and there is increasing interest in expanding the industry. The krill fishery employs different trawl designs and is not currently subject to technical regulations specifying the types of fishing gear and mesh sizes that can legally be used. There is a need to establish a robust model predicting size selectivity that includes the morphological variation in the population of krill. Male and female Antarctic krill are described with 12 maturity stages, from juveniles to sexually mature adults, each with distinct morphological features. The current study established a morphological description of each individual krill maturity stage to identify and parameterize what determines size selectivity using the FISHSELECT framework. This framework is used to predict size selectivity for each of the different stages in various mesh sizes and openings relevant to the krill fishery, in both actual and virtual populations. The results can be used to assess size selectivity for specific fishing gears and population structures, facilitating more accurate understanding and modeling of the fishery’s impact on the demographic composition of the krill stock.

The fishery for Antarctic krill (Euphausia superba) is the largest by tonnage in the Southern Ocean, and understanding its population dynamics is essential for the sustainable management of this fishery. The standard method for calculating Antarctic krill biomass relies on hydroacoustic survey data and incorporates krill body length data collected concurrently. Traditional scientific acoustic surveys involve manually measuring the body lengths of individual krill caught using fine- meshed nets or trawls along acoustic transects. This work is resource-demanding and could represent a source of human error. To address these challenges, we develop and test an alternative, more automated method for estimating krill body length data by employing an in-trawl stereo camera system. This system collects images that are automatically processed by a custom-trained machine learning model. The results from the machine learning model are then compared to manually measured krill subsampled from the total catch of the corresponding trawl hauls. We demonstrated the ability to extract body lengths from underwater images. However, our results highlighted uncertainties, which we propose addressing by incorporating more advanced camera technology and optimizing the observation section of the small-meshed two-layer krill trawl.

Antarctic krill (Euphausia superba) are integral to Southern Ocean pelagic ecosystems. Winters with extensive sea ice have been linked to high post-larval krill recruitment the following spring, suggesting that sea ice plays a critical role in larval overwinter survival. As the ocean warms and sea ice declines under climate change, understanding the mechanisms linking sea ice and krill recruitment is increasingly urgent. To address this, we developed a qualitative network model (QNM) that integrates evidence-based and hypothesized interactions to explore larval overwinter survival and growth under future climate scenarios in the southwest Atlantic sector. Our model highlights habitat-specific impacts, with substantial declines predicted for the North Antarctic Peninsula continental shelf due to reduced autumn primary productivity and warming. In contrast, survival may improve in open-ocean habitats under cooler scenarios that enhance sea-ice-associated processes, such as food availability and refuge. The inclusion of hypothesized mechanisms, such as sea-ice terraces providing refuge from predation, strengthened these conclusions and highlighted critical uncertainties, including the influence of glacial melt on food web dynamics. These findings demonstrate the value of QNMs in complementing quantitative approaches, offering a framework for identifying critical mechanisms, addressing knowledge gaps, and guiding future field and laboratory studies to improve predictions of krill responses to climate change.

Antarctic krill meal (KM) (Euphausia superba) as a substitute for fishmeal in aquatic animal diets is gaining popularity worldwide. A quantitative approach investigating the efficacy of using this protein on the production performance of aquatic animals remains widely limited. Here, we employed a meta-analysis to quantify the overall effects (Hedges’g [g] value effect size) of KM on the specific growth rate (SGR), feed conversion ratio (FCR), protein efficiency ratio (PER), and survival rate (SR) of several aquaculture species. A total of 22 records published during 2006 to 2022 from different countries, targeting 14 aquatic species, were employed in the present study. Overall, KM has a high nutritional value relative to fishmeal, particularly from the high protein and amino acid composition. Dietary KM significantly increased the overall effect size of SGR (g = 1.92) (P = 0.001); the positive effect was illustrated in marine species (g = 1.32 to 9.10) (P < 0.05) and sturgeon (Acipenser gueldenstaedtii) (g = 6.59) (P < 0.001). The overall g value for FCR (−2.42) was significantly improved compared to the control group (P < 0.001). The inclusion of KM in aquatic animal diets did not affect g value of PER (1.52, 95% confidence interval: −1.04 to 4.07) and survival rate (0.08, 95% confidence interval: −0.63 to 0.79) (P = 0.252 and 0.208, respectively). The meta-regression models indicated that SGR of rainbow trout (Oncorhynchus mykiss) was significantly correlated with dietary KM by a positive linear model (P = 0.022). The cod and sturgeon (A. gueldenstaedtii) appeared to efficiently utilize krill-containing diets as illustrated by a negative linear model (P = 0.011 and P = 0.024, respectively) between dietary KM and FCR. Dietary KM positively correlated with PER for Atlantic cod (P = 0.021). Our meta-analysis highlighted the significant outcome of KM in diets for aquaculture species by reducing pressure on forage fish from marine resources and sparing edible foods. Specifically, including KM significantly reduced economic fish-in fish-out (eFIFO) in four taxa—the top forage fish consumers (P < 0.05): marine fish, salmon, shrimp, and trout. The meta-analysis revealed the decreased food-competition feedstuff in diets for important aquaculture species (P < 0.05) fed dietary KM. The outlook for efficient use of KM from marine resources in aquafeeds was elucidated in the present work.