Ukrainian vessel CALIPSO (Fig. 1) (shipowner FC NEPTUNO LLC, Ukraine) performed the research survey in the statistical subarea 48.1 according to the research plan SC-CAMLR-39/BG/08 and management advices of the WG-FSA-2020 and SC-CAMLR-39 in the second part of February 2021. The survey was carried out partly and stopped in advance due to the exhaustion of the limit (a total of 6.56 tons were caught from the allocated 7 tons) for fish species Macrourus spp.

The problem of differences more than 10% between vessel catch reporting form (C2) and landed Dissostichus catch (DCD) on Ukrainian fishing vessels Simeiz, Koreiz and Calipso was raised  by the CCAMLR Secretariat in 2018, and the data from these Ukrainian fishing vessels for the period 2014-2018 has been quarantined. There is proposed to include these data, revised by Ukraine, into the CCAMLR database (with special mark), leaving the uncorrected  data with the appropriate mark also available for use.

In RB5, the SICs in Feb. 2021 were the highest and the SSTs were the lowest for the years 2016-202.  In Mar.  2021, the highest SICs decreased to nearly the long-term average while the SST increased accordingly. In the same year, the SICs and SSTs had two peaks in Feb. and Mar. respectively.  In RB4, the SICs during Jan.- Feb (Austral summer) in 2021 were also the highest since 2016. The sharp spikes of SST (rapid increasing SST) had become smaller year by year from 2017 to 2021, which indicates that the SSTs had a cooling phase in 5-6 year periodical cycles corresponding to an increasing trend in SICs.

Spatial dynamics of SICs with SSTs contour of -1.8°C and -0.8°C were analyzed. It was found that the ice edges are at approximately -1.8°C and partially broken ices exist between -1.8°C and -0.8°C when comparing imagery by GIBS and SICs distribution by AMSRs with SSTs by NOAA.

Daily wind stick plots indicate that the eastward winds could encourage the off-shore Ekman transport at the end of Feb. and the beginning of Mar. which resulted in late (slow) ice retrieval in 2021.

Recommendations

When modelling multiple fleets in a GYM assessment, we recommend the use of the presented extension to the Grym package based on its shown capacity to model more complex fisheries and fishing practices with a high degree of consistency in results.

Abstract

Grym is an implementation of the Generalized Yield Model in R that provides greater transparency and extensibility (Maschette et al., 2020, Wotherspoon & Maschette, 2020). This paper describes an extension of the Grym to permit multiple fleets within a season, allowing the Grym to model more complex fishery behavior and evolving fisheries practices. Results from the expansion are compared with existing analyses from the Grym. The model can include vessels using different gear types (e.g. trawl and longline) or identical selectivity. An example is presented for Patagonian toothfish.

Recommendations

That WG-SAM note these preliminary explorations of alternative decision rules for managing toothfish fisheries, which indicate that an approach based on a harvest rate H:

• Would be consistent with the CCAMLR decision rule and its objective, and;
• Could provide a higher level of predictability and certainty about the likely changes in future catch limits, and reduce fluctuations in stock size around the target level in the long term.

Abstract

In this paper, we conducted simple simulations to outline alternative decision rules with the potential to manage toothfish fisheries according to the current CCAMLR decision rule. In contrast to the current decision rule, the rules in this paper were based on a harvest rate H which was stochastically estimated from the stock productivity and fishery selectivity to result in the long-term 50% SSB depletion with a probability of 50%. Catch limits were then calculated from the harvest rate, the fishery selectivity and the current vulnerable stock biomass, bound by minimum and maximum levels of change to reduce short-term variability in the catch limit. We evaluated two different types of decision rules (constant H and hockey-stick response function for H). All evaluated scenarios demonstrated that:

• The SSB target level was reached in around 35 years in all simulations, indicating that an approach based on a harvest rate would be consistent with the CCAMLR decision rule and its objective;
• The expected catch limit variability between assessments would be around 5–10% on average and rarely hitting the 20% maximum level used here.

An alternative rule based on a harvest rate could provide a higher level of predictability and certainty about the likely changes in future catch limits, and reduce fluctuations in stock size around the target level in the long term.

Recommendations

For GYM assessments with proportional recruitment, we recommend to use:

• Formula-based methods for low variance in proportional recruitment;

• Simulation modelling for high variance in proportional recruitment to accurately reproduce mean and standard deviation;

as described in this paper and implemented in the Grym (open source package).

Abstract

Krill are a keystone species in the Southern Ocean food-web, and, as such, it is crucial to effectively manage the krill fishery to ensure its long-term sustainability. To assess the impacts of current harvesting pressures, evaluations rely on sampling and population modelling. Krill stock projections are developed with the Generalised Yield Model (GYM), which provides an assessment for stock status under current harvesting scenarios and various levels of uncertainties. One of the fundamental components of the GYM is the simulation of recruitment. De la Mare (1994) presents a proportional recruitment function for estimating krill recruitment based on estimates from field surveys. The De la Mare (1994) function uses estimates of the mean and variance of recruitment from survey data to determine the scaling of natural mortality and the distribution of random recruits that reproduce the observed mean and variance estimates. We evaluated De la Mare’s (1994) proportional recruitment function and found that for large variations in recruitment the function does not reproduce the observed mean proportion of recruits and its variance accurately. We review the deficiencies within the model and provide two alternative methods, which can support a wider range of values and possible extreme scenarios, such as years of low recruitment.

As part of the revision of the krill management approach, the Secretariat was tasked to estimate proportional recruitment indices in Subareas 48.1, 48.2 and 48.3 using the SISO (i.e., observer) krill length data collected onboard fishing vessels. This document summarizes the steps taken to estimate the mean and standard deviation of proportional recruitment in each Subarea, for potential use as inputs to the Grym (Maschette et al., 2020).

Preliminary biomass estimates for research blocks in data-limited fisheries and the resulting preliminary catch limits for the 2022 season have been determined using the trend analysis decision rules. The resulting preliminary catch limits are for illustrative purposes only and do not constitute recommendations.