The length distribution and biological indicators (weight, sex, maturity phases and nutrition indicators) of Antarctic krill obtained  during the Russian complex survey on the RV "Atlantida" in January-March 2020 are shown. Analysis is based on 179 standard  double oblique tows with research trawl.  The length distribution and biological indicators were analyzed by strata including the South Shetland Islands stratum (SSI), the Bransfield Strait stratum (BS), the Elephant Island and the Joinvile Island strata (EI), the Scotia Sea stratum (SS).   The catch basis was formed by mature and post-spawning krill individuals, females of stage IIIA and IIIE, males of stage-IIIB.

This report summarizes the spatial layers developed to date to support the development of the Weddell Sea Marine Protected Area Phase 2.  We link these spatial layers to the conservation objectives complementary to the Weddell Sea Marine Protected Area Phase 1 and preliminary specific conservation objectives for the Weddell Sea Marine Protected Area Phase 2. We include a summary of further considerations from expert discussions at and since the WSMPA Phase 2 International Scientific Workshop (digital) 10-12 May 2021. A summary report of this workshop is provided as supplementary material.

While the importance of spatial scale in ecology is well established, few studies have investigated the impact of data grain on hotpot analysis of biological resource. In this study, in order to analyze the impact of spatial scale on hotspot analysis of Antarctic krill (Euphausia superba) density distribution in the Southern Ocean, the krill resource density data in 1926 to 2016 by 10-year period was interpolated into ten spatial scales, i.e., 10′×10′, 20′×20′, 30′×30′, 40′×40′, 50′×50′, 1°×1°, 2°×2°, 3°×3°, 4°×4°, and 5°×5°, respectively, and linear, logarithmic, exponential, power-law, and polynomial functions were used to calculate the relationship between the global krill resource density, krill resource density in hotspot (coldspot) areas and the spatial scale, and the variations in centroid and hotspot (coldspot) areas at different spatial scales were also analyzed. The results showed that there was a significant scaling relationship between the maximum, skewness, kurtosis, Coefficient of Variation (CV) of global krill resource density and the spatial scale. Significant scaling relationship between the maximum, skewness, kurtosis, third quartile (Q3), and CV of the krill resource density in hotspot area and the spatial scale was found. A significant scaling relationship were also found between the maximum, mean, standard deviance (SD), skewness, kurtosis, Q3, CV and spatial scale for the coldspot area. The size of the hotspot (coldspot) area increased with the increasing of the spatial scale, and the offset of centroid indicated the locations of the hotspot (coldspot) area were greatly affected by the spatial scale. Significant shift of centroid for hotspot (coldspot) areas occurred when the spatial scale was greater than 1°×1°. Therefore, it is recommended to use spatial scale that less than 1° × 1° to identify the local spatial pattern for hotspot analysis of krill resource density.

The coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This pathogen has spread rapidly across the world, causing high numbers of deaths and significant social and economic impacts. SARS-CoV-2 is a novel coronavirus with a suggested zoonotic origin with the potential for cross-species transmission among animals. Antarctica can be considered the only continent free of SARS-CoV-2.  Therefore, concerns have been expressed regarding the potential human introduction of this virus to the continent through the activities of research or tourism to minimise the effects on human health, and the potential for virus transmission to Antarctic wildlife. We assess the reverse-zoonotic transmission risk to Antarctic wildlife by considering the available information on host susceptibility, dynamics of the infection in humans, and contact interactions between humans and Antarctic wildlife. The environmental conditions in Antarctica seem to be favourable for the virus stability. Indoor spaces such as those at research stations, research vessels or tourist cruise ships could allow for more transmission among humans and depending on their movements between different locations the virus could be spread across the continent. Among Antarctic wildlife previous in silico analyses suggested that cetaceans are at greater risk of infection whereas seals and birds appear to be at a low infection risk. However, caution needed until further research is carried out and consequently, the precautionary principle should be applied. Field researchers handling animals are identified as the human group posing the highest risk of transmission to animals while tourists and other personnel pose a significant risk only when in close proximity (< 5 m) to Antarctic fauna. We highlight measures to reduce the risk as well as identify of knowledge gaps related to this issue.

SKAG was initiated in close collaboration with the Scientific Committee on Antarctic Research (SCAR) Standing Committee on the Antarctic Treaty System (SC-ATS) to help provide the scientific information on krill needed to manage the krill fishery by improving communication between CCAMLR and the wider krill science community. In addition, the group serves as a platform for early career reserachers (ECR) to network with established krill researchers.The first phase of SKAG (2018-2020) resulted in a paper (Meyer et al. 2020) that identified knowledge gaps in krill ecology which are important for krill fishery management. The paper also outlined the data and methods needed by the scientific community, in collaboration with the krill fishery, to fill these knowledge gaps.

The priorities which Meyer et al (2020) identified for krill research to support ecosystem-based management of the krill fishery are; (i) unravelling the controls on krill recruitment, (ii) pinpointing spawning hotspots that merit protection,(iii) identifying seasonal overlaps between the fishery and contributing spawning stock, and (iv) future-proofing fishery management for climate change.

In the second SKAG phase (2021-2023), we aim to engage the broader science community to evaluate the key research priorities and suggest ways forward. The first step in this direction was a one-week online workshop (26-30 April) organized in cooperation with the WWF. This workshop was attended by around 100 participants each day from 19 countries representing a large portion of the world’s krill expertise. The workshop was closely linked with the following workshop of the SCAR Program Integrating Climate and Ecosystem Dynamics of the Southern Ocean (ICED). For details of the ICED workshop see their report submitted to WG-EMM 2021.

This report provides overview and early results of the multidisciplinary large-scale survey of the Eastern Sector of CCAMLR Division 58.4.2 conducted in February to March 2021. The survey consisted of six major acoustic line-transects to estimate krill biomass and to observe swarm behaviour across ecological and density gradients within the survey area south of 62°S between 55° and 80° E, with a single fine-scale krill box acoustic survey off the Mawson coast. The voyage successfully estimated mean areal biomass for the region. Along with acoustic survey we conducted net sampling, deployed swarm study system, deep-sea camera and light trap, deployed Krill Observational Moorings for Benthic Investigation (KOMBI)  on the seafloor that will record the behaviour of krill throughout a full year to understand the dynamics and use of habitat in the surface layer as well as at seafloor. Comprehensive oceanographic (CTDS, XBTs, ARGSO floats) and biological (plankton) sampling were also conducted to understand the habitat environment of krill and its predators. Predator observation was undertaken throughout the voyage to improve our understanding on predator abundance, distribution, and its relationship with krill distribution and their habitat environment. The information gathered contributes to the design of tractable and sustainable long-term monitoring plan and to evaluate spatial management of the krill fishery.

Two independent lines of evidence have been presented to the working groups and SC-CAMLR that claim to demonstrate that fishery-driven localised depletion of krill around pygoscelid penguin colonies has had a deleterious effect on their performance traits and demographic trends, that are equivalent to the impacts of climate variation. One study utilises 30 years of penguin foraging and reproductive performance of penguins in relation to krill biomass using 30 years of data collected at two colonies in the South Shetland Islands, whereas the other uses demographic rate changes derived from a comprehensive dataset of penguin population count data across Subarea 48.1 matched against acoustic measurements of krill biomass and krill catches at the gSSMU scale (Watters et al., 2020). The second uses estimated population trajectories across a wide range of penguin breeding colonies in relation to krill catches within a 30km radius (Krüger et al., 2021). Both studies then explore the synergistic relationships to measurements of broad-scale climactic variation (El Niño-Southern Oscillation; ENSO, and the Southern Annular Mode; SAM). Herein we provide a preliminary assessment of the efficacy of both approaches in drawing conclusions, that are now being used at the Commission level, as representing sound scientific advice. We demonstrate that several underlying assumptions in Watters et al. 2020 are contrary to the published scientific literature, and when the model syntax is re-written to reflect these, predicted penguin performance against long term expected means are substantially different to those presented to CCAMLR. The evidence provided by Krüger et al. (2021) uses a different analytical approach, however given the details provided we were unable to recreate the initial results and could not test the sensitivity of the model to some of the assumptions made. We do, however, point to areas in which we have concerns, and would welcome collaboration in order to clarify and address these through a more in-depth future analysis. Overall while our preliminary assessment focuses on potential issues, future work will centre on considering competitive interactions both at appropriate time and space scales between the fishery as well as between a range of krill dependent predators beyond just pygoscelid penguins.

Fine-scale knowledge of spatiotemporal dynamics in cetacean presence and abundance throughout the Antarctic Peninsula (AP) is lacking yet essential for effective ecosystem-based management (EBM). We used cruise vessels as platforms of opportunity to investigate an important area both for migratory humpback whales (Megaptera novaeangliae) and Antarctic krill (Euphausia superba) fisheries to assess potential spatiotemporal interactions, for future use in EBM. Whale observations were collected using tourist cruise ships as platforms of opportunity during the austral summer of 2019/2020. Data were analyzed using both traditional design-based line transect methodology and spatial density surface hurdle models to estimate the abundance and distribution of whales in the area, and to describe their temporal dynamics. The latter were fitted using a set of physical environmental covariates (sea surface temperature, ocean filaments, bathymetry and their derivatives). Our results indicate that few humpback whales are present in the Bransfield and Gerlache Straits in early December, however, their abundance increase rapidly to late December in the northern Gerlache Strait, reaching a stable abundance by mid-January. The distribution of humpback whales appeared to change from a patchier distribution in the Gerlache Strait to a significantly concentrated presence in the northern Gerlache and southern Bransfield Straits, followed by a subsequent dispersion throughout the area.  Depending on the modelled abundance, estimates agreed well with previous literature, increasing from approximately 7000 individuals in 2000 to a peak of 19 107 in 2020. Based on these estimates, we project a total krill consumption of between 2.0 and 5.2 million tons based on traditional and contemporary literature, respectively. Based on our results and catch data in the study area, we conclude that there is minimal spatiotemporal overlap between humpback whales and krill fishery activity during our study period of November – January. However, there is potential for significant interaction between the two later in the feeding season, but cetacean survey efforts need to be extended into late season in order to fully characterize this potential overlap.

We present here a summary of the euphausiid larvae collected during the summer seasons in waters off the West Antarctic Peninsula: Gerlache Strait and surroundings of South Shetland Islands in 2017-2018; Mar de la Flota (Bransfield Strait) and Elephant Island surroundings during 2019 and 2020. Euphausia superba larvae showed a very low abundance in 2017. In general terms the stations situated at the southern entrance of the Gerlache Strait presented higher densities. In 2018 E. superba was present in half of the stations sampled. The abundance of Thysanoessa macrura remained stable in both years, with higher numbers in 2018. During 2019 in Mar de la Flota (Bransfield Strait) area E. superba abundance was very high, while during 2020 all euphausiid larvae had very low densities, as the whole area was full of salps.

This paper responds to a request from the Scientific Committee for the Secretariat to review the data that has been submitted for estimating the green-weight of krill, for each of the methods specified in CM 21-03, Annex 21-03/B (SC-CAMLR-38, paragraph 3.4). Results from the analysis demonstrate a good relationship between reported green-weights and the calculated krill green-weights using the estimation parameters, with exception of two vessels’ data reported in seasons 2014 and 2015. Conversion Factors were also investigated, and results show that considerable variation exists for reported figures associated with particular processing type and direct green-weight estimation combinations. A number of recommendations are provided to potentially address these issues.