CCAMLR

The Antarctic Peninsula is one of the most rapidly warming regions on earth, and it is likely that the abundance and distribution of marine predators will change as a result.Procellariiform seabirds are highly mobile predators, which target specific habitat characteristics associated with underlying distributions of prey and areas of increased prey availability. We use ship surveys and hurdle models, to estimate the summer distribution and relative density of 11 seabird species within the northern Antarctic Peninsula marine ecosystem. Models differed among species; however, sea surface temperature and depth were frequently associated with seabird occurrence and had the greatest explanatory power across many species. Null models based on observation data were better at predicting seabird density than models that included environmental covariates. This suggests that the main driver of distribution patterns is the broad-scale habitat features, and fine-scale aggregations within these ranges are harder to predict. Our seabird distribution models reflect known habitat associations, species hotspots, and community organization relative to oceanic and coastal marine processes. Application of species distribution models will benefit the assessments of critical habitat and potential responses to climate change and anthropogenic disturbance, which will provide insight into how species may change in polar ecosystems.

Ecosystem dynamics at the north-west Antarctic Peninsula are driven by complex interactions between physical and biological processes. For example, baleen whale populations are recovering from commercial harvesting against the backdrop of rapid climate change, including reduced sea-ice extent and changing ecosystem composition. Concurrently, the commercial demand for Antarctic krill is increasing, with the potential to increase the likelihood for competition with and between krill predators. However, understanding of the ecology, abundance, and spatial distribution of many krill predators is often limited, outdated, or at spatial scales that do not match those desired for effective fisheries management. We update current knowledge of predator dependence on krill resources by integrating recent telemetry-based data, at-sea observational surveys, regional estimates of predator abundance, and physiological data to estimate the spatial distribution of krill consumption during the austral summer by three species of Pygoscelis penguin, 11 species of flying seabirds, and one species of baleen whale (humpback whale, Megaptera novaeangliae). Our models show that the majority of important areas for krill-predator foraging are close to penguin breeding colonies in coastal areas where humpback whales also regularly feed. We show that krill consumption is highly variable across the region, and often concentrated at fine spatial scales, emphasising the need for management of the local krill fishery at relevant temporal and spatial scales. We highlight that despite less than comprehensive data, cetaceans are likely to consume a significant proportion of the krill consumed by natural predators, but are not currently considered directly in the management of the krill fishery. If management of the krill fishery is to remain precautionary and operate in a way that minimises the risks to krill predator populations, it is necessary to include up-to-date and precise abundance and consumption estimates for seals, fin-fish, squid, and other baleen whale species not currently considered.

Antarctic krill are an important component of the Antarctic marine ecosystem, providing a key food source for many marine predator species. Additionally, krill are the target of the largest commercial fishery in the Southern Ocean, for which annual catches have been increasing in recent years. The krill fishery is managed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), which has recently endorsed a new management framework that requires information about the spatial distribution and biomass of krill. Here, we model the seasonal (summer and winter) spatial habitat use of krill across the northern Antarctic Peninsula region, an area important to the commercial fishery. Using krill density estimates obtained from historical acoustic surveys and a GAMM framework, we model habitat properties associated with high krill biomass. During summer, krill density is elevated around Elephant Island and to the north of the South Shetland Islands, and our models show associations with the shelf break, increased sea-surface temperature, moderate chlorophyll-a concentration and increased salinity. During winter, krill density is elevated in the nearshore waters of the South Shetland Islands, and our models show associations with shallow waters with low sea-ice concentration, medium sea level anomaly, and medium current speed. Our models predict temporal averages of the distribution and density of krill, which can be used to aid the development of CCAMLR’s revised ecosystem approach to fisheries management in this region. We emphasise that our models provide descriptors of habitat characteristics, and do not necessarily identify key drivers of krill distribution. However, our models do have the potential to help in the spatial and temporal design and placement of future acoustic surveys that would preclude the future need for modelled extrapolations. We highlight that the ecosystem approach to fisheries management of krill critically depends upon such field observations at relevant spatial and temporal scales.

We have applied the risk assessment framework, developed by Constable et al. (2016), to Subarea 48.1, with the aim of identifying the most appropriate management units by which to spatially distribute the local catch limit for the commercial fishery for Antarctic krill. We use the best available data for implementing the approach which was endorsed by the Commission in 2019. The framework is flexible and can accommodate new data to improve estimates of risk in the future.

We evaluated 30 catch distribution scenarios for assessing risks from krill fishing in Subarea 48.1. For each scenario we calculated the regional baseline risk, and the regional desirability risk. Baseline risk is defined as the risk to predators and krill and is based on predation pressure and the proportion of juvenile krill in each management unit. Desirability risk is defined as the risk to predators and krill as for the baseline risk, but also accounting for the desirability of a management area to the fishery i.e. more catch may be attributed to areas where the fishery has previously fished (desirable areas) than in the baseline scenario. We show that the spatial distribution with which the fishery currently operates presents the highest risk of all scenarios evaluated. Managing the fishery at much smaller scales has the lowest risk but may necessitate a high level of management interaction with the fishery.

This implementation can provide advice to CCAMLR for short-term management and could provide a template for the rest of Area 48. We highlight that each data layer impacts the outcome of the risk assessment and recommend that updated estimates of the distribution, abundance and consumption of krill, and estimates of available krill biomass will be key as CCAMLR moves forward to develop a longer-term management strategy.

A benefit of the risk assessment framework is that CCAMLR now has a tool for direct comparison of risks associated with alternative catch distributions at an appropriate spatial scale for management. We suggest one approach for choosing between scenarios, based on regional risk (either baseline or desirability). We recommend that WG-EMM might consider the approach outlined and decide whether other tools may also be appropriate.

We provide advice about the scale at which the krill fishery can be managed, but highlight important issues that should be discussed, including uncertainty, before CCAMLR agrees the design of spatial management units.

Finally, we highlight that our endeavours have been the result of a community effort and we are grateful to those that have provided data and advice.

Data  from catches of research trawl  were obtained in Subareas 48.1 and 48.2  during cruise  RV Atlantida  in 2020.  The number of krill measured (length, weight) from 179 trawl catches was about 20,000. The length ranges smaller 36 mm were used to define the proportional recruitment. Variability of krill length frequency distributions and proportional recruitment by stratum are shown. The proportional recruitment of krill in Bransfeed Strait stratum (BS) and the Elephant Island (EI) stratum was 0,617 and 0, 472, respectively. Variability of weight - length relationship by stratum were also obtained, and these relationship differ from  the known regression ( a=2, 236 x 10^-6 , b=3,314)  that are used  in CCAMLR practice ( for example,  CCAMLR 2000 Survey, Survey 2019, RV Atlantida 2020; and other surveys). The sensitivity of conversion factor to the used weight-length relationship is shown. The use of the known relationship (a=2, 236 x 10^-6, b=3,314)   may underestimates the conversion factors. It was shown that the conversion factors when using this relationship in comparison with the new relationship will result in underestimations of the krill density from 10 to 26% depending on the stratum. To our opinion it is necessary to revise the use weight-length relationships.   

As part of the revision of the krill management approach, an updated set of parameter values is required for the Grym to perform projections in Subareas 48.1, 48.2 and 48.3. Where applicable, Subarea-specific values are set to account for the different dynamics in each Subarea. Given the scope of the task, some parameter values are found in the scientific literature while others correspond to the current expertise from all involved CCAMLR scientists. This document summarizes discussions held in the "GYM/Grym assessment model development” e-group.

The Scientific Committee considered the assessment of Dissostichus spp. in data-poor fisheries to be of a high priority (SC-CAMLR-XXIX, paragraphs 3.125 to 3.145). The use of different gear types for the implementation of a multi-Member researches on Dissostichus spp.  in the East Antarctic region (Divisions 58.4.1 and 58.4.2) carried out in the seasons 2011/12 - 2017/18  is a critical factor for efficiency and reliability  of these multi-Member researches. In the context of the discussion of the Scientific Committee (SC-CAMLR-XXXVII p.3.338-3.144; SC-CAMLR-XXXVIII p. 318, 3.119; SC-CAMLR-XXXIX p.4.10) we propose the research program on Dissostichus spp. by the multi-vessels in Divisions 58.4.1 and 58.4.2 from 2021/22 to 2023/24 based on standardized   sampling longline gear and survey stratified  design.   

This paper examines fishery data collected by Russian scientific observers on longline vessels that fished toothfish using Spanish longline and trotlines in CCAMLR and adjacent Atlantic waters during the 2002-2017 fishing seasons. The factors influencing the longline fishing impact zone    are discussed in this paper.  It is shown that the existing approach to the definition of "fishing impact zone" is aimed at determining the impact of fishing on the bottom. This is primarily important for assessing the risk areas where fishing can impact on VMEs.  However, the assessment of the CPUE, the understanding of the fish size composition in the catches requires an expanded understanding of the term "fishing impact zone". It should include both the interaction of the longline with the bottom, and the presence of a bait smell field that attracts fish to the longline.

Stock Annex for the 2021 assessment of the Antarctic toothfish (Dissostichus mawsoni) population of the Ross Sea region, including stock structure and definition, fishery information, catch data, biological information, abundance information, and stock assessments.

Here, we provide diagnostic plots for the 2021 assessment model for Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea region presented in Grüss et al. (2021a), following the recommendations of WG-SAM-2015 (SC-CAMLR-XXXIV 2015 Annex 5). The stock assessment model is described in Grüss et al. (2021a), and a detailed description of the stock area, stock assessment methods and the stock assessment parameters are given in Grüss et al. (2021b).