CCAMLR

This study reports some preliminary results of the impacts of Ocean Acidification due to elevated seawater pCO₂ on the activity, mortality, moulting of post-larval krill.  Increased CO2 levels increased mortality, reduced activity levels and fitness, and caused moult cycle irregularities. No significant effects on growth rates and intermoult period were observed under experimental conditions in this study. Some practical approaches to observing and improving our understandings of the impacts of OA on Antarctic krill population are suggested.

This document describes the aims and methodology of a three year project (commenced in 2012) entitled Net Escapement of Antarctic krill in Trawls (NEAT). The study will include a morphology based mathematical modeling (FISHSELECT) of different sex and maturity groups of Antarctic krill (Euphausia superba) to predict basic selective characteristics of different trawl gear and net designs. Results will be used to quantify the theoretic catch efficiency and escape mortality in different net designs and also to construct design guides, to minimize escape mortality, with descriptions of basis selective properties for krill in different trawls.

We recalculated krill biomass for the 2006 BROKE-West (CCAMLR Division 58.4.2) summer survey.  The recalculation was carried out on the calibrated, multi-frequency acoustic data described in Jarvis et al. (2010) Deep-sea Res. Pt II 57:916-933.  Biomass recalculation methods conformed to the CCAMLR guidelines - issued in 2010 via SG-ASAM.  Our revised estimate of mean areal krill density is 20.5 gm^-2 (CV = 0.16) cf. 21.9 gm^-2 (CV=0.16) of Jarvis et al. (2010), which equates to a krill biomass, B₀, of 28.43 (95% CI = 19.93 to 36.93) Mt cf. 28.75 (95% CI =19.63 to 37.87) Mt of Jarvis et al. (2010). Both the revised and Jarvis et al. (2010) point and variance estimates are practically identical.

We examined the within- and between- year fluctuations of four Adélie penguin population parameters which are thought to be sensitive to changes in prey availability: breeding success, foraging trip duration, meal mass and fledgling weights. Some years had either good breeding success with heavy fledglings or poor breeding success with light fledglings while others had a lack of concordance between breeding success and fledgling weight. These discordant years also had an inconsistency between the duration of early and late stage foraging trips. For example, low breeding success was recorded in a season with long foraging trips during the guard period, relatively short trips during the crèche period and heavy fledglings. These results may indicate changes in the relative level of resource availability between the guard and crèche stages of the breeding season (i.e. was relatively low during the guard stage and elevated during the crèche stage). The overall temporal variability across years in response parameters was much greater than was observed in the two years with concurrent krill abundance. If the temporal variability in predator response parameters at this site is largely driven by changes in prey availability then these results would add further weight to significant changes in predator response only occurring with changes in krill availability at low levels (ie the Hollings type II shape curve). Our results highlight the importance of taking into account the changing behaviours of birds in the context of life history requirements, changes in prey accessibility as well as any temporal variability in the amount of prey present when interpreting predator response parameters.

Benthic communities of the Terra Nova Bay area (Ross Sea) close to the Italian station Mario Zucchelli is located, have been largely investigated in the last couple of decades. All the acquired knowledge supports the presence of very rich and diversified communities. This has led to the establishment of an ASPA (N° 161).

The Antarctic scallop, Adamussium colbecki (Smith, 1902), is one of the most abundant and conspicuous species in the area, although some of its denser populations are found outside the boundaries of the ASPA.

These populations have been widely investigated and still are, under different perspectives. Particularly interesting is the evidence that populations in Terra Nova Bay area seem to be genetically different to some extent, and are even more different from those in the McMurdo Sound area, notwithstanding the planktotrophic behavior of the larva and the purported long pelagic life stage. These finding reinforce the concept that this scallop, locally very abundant but discontinuously distributed, must be included in the VME taxa list and support the need to consider the scattered populations as VMEs, being potentially genetically isolated from each other.

In 2005-2011 the Ukraine delegation at the CCAMLR presented to the Scientific Committee a series of documents with a proposal to establish Marine Protected Area (MPA) in the Akademik Vernadsky Station area with the purpose of protecting natural and geographical objects and establish test areas of special scientific interest. We present the results of the first steps in creation of Marine Protected Area network in Argentine Island Archipelago region.

AMLR krill sampling data was supplied to the Generalised Yield Model as the "vector of recruitments" input option to simulate the population dynamics of krill in the Antarctic Peninsula region (Subarea 48.1) under various assumptions. The annual proportions of krill less than 36 mm in length to the total captured in AMLR net samples in four sampling regions of the Antarctic Peninsula were used as proxies for recruitment variability. Simulations were run for 21 years with either no fishing, or with fishing at either the trigger level (gamma = 0.0103), the precautionary catch limit (gamma = 0.093), or half the precautionary catch limit (gamma = 0.0465). Simulations were assigned natural mortalities at either the "base case" value (M = 0.8, or 45% annual survival), "variable mortality" (M with a uniform distribution between 2 and 0.8, or annual survivals varying between 14 and 45%) and "high mortality" (M = 3, annual survival of 5%). CVs of either 0, 10%, 20%, or 30% were added to the observed recruitment values.

The CCAMLR "depletion" decision rule was more susceptible to being triggered by these modifications to the GYM inputs than was the "escapement" rule. For the base case simulations with M=0.8 and additional recruitment CVs of 0, simulated populations based on recruitment vectors from all four sampling areas were able to support the trigger level of catch with a less than 10% chance of the population spawning biomass falling below 20% of its unfished value, meeting the CCAMLR "depletion" decision rule for recruitment. At the higher catches of the precautionary catch limit, populations based on recruitment vectors from two of the four areas, Elephant Island and the Western Area, were unable to support the catch while maintaining spawning biomasses above 20% of unfished biomass in more than 90% of the trials. As the values for natural mortality and additional recruitment variability were increased beyond the "base case" values, fewer of the simulation scenarios were able to achieve the CCAMLR "depletion" decision rule.

Estimates of population parameters from different configurations of an integrated model under development for Antarctic krill are compared to estimates from CMIX and TrawlCI using the same net trawl data. The different configurations of the integrated model were varied based on whether acoustic data or net densities were used as model inputs for the biomass data, on how the biomass data were weighted, by whether natural mortality was estimated by the model or pre-assigned a value of 0.8, and by whether a narrow or wide version of the age-length transition matrix was used. The biomass data calculated for most combinations of samples grouped by year, area, and leg was somewhat lower when it was extrapolated from net densities than when biomass was calculated from acoustics. This was reflected in lower model estimates of total population abundance for most years and areas when the input data were based on net biomass densities in either TrawlCI or the integrated model than when acoustic measures of biomass were supplied as data to the integrated model. All estimates from the integrated model but not TrawlCI included selectivity parameters for the effects of differential availability to the surveys of different ages in the population. Thus the integrated model estimated two population sizes, the "vulnerable" population based on the relative availabilities of individuals of different ages to the surveys, and the total population including the individuals not available to the surveys. The "vulnerable" population sizes in the integrated models were usually more similar to TrawlCI estimates of population abundance than to the integrated estimates of total population abundance, particularly when acoustic measures of biomass were used to inform the integrated model. Time relationships in the length compositions were more evident in the integrated model estimates than in those from CMIX. Modifications to improve the estimation of selectivity parameters when multiple sources of biomass survey data are available are continuing.

This report presents the results of a method used to explore potential explanatory variables influencing finfish bycatch in the krill fishery of Area 48. Records of finfish bycatch in the Area 48 krill fishery collected by observers on the FV Saga Sea were analysed over the period 2007/08-2011/12. The majority of fish caught were either small juveniles or larvae, dominated by Myctophidae (lanternfish) and Channichthyidae (icefish) with lower levels of Nototheniidae present. The influence of potential explanatory variables was investigated using a delta-lognormal modelling approach. Time of day, krill catch, sea surface temperature, bottom depth and fishing depth and season were all significantly associated with the presence of finfish bycatch in the Saga Sea krill fishery for at least one family and Subarea specific model, however, the majority of variables were not significantly correlated with the abundance of finfish bycatch. This may be partly due to the low numbers of hauls with positive incidences of bycatch of the finfish family groupings. Results indicated that there was a wide disparity in the influence of the explanatory variables on the presence of finfish in bycatch, which varies markedly by taxonomic grouping to the family level and CCAMLR Subarea. There were, however, some trends which persisted across Subareas and families, the most notable observed trend being the reduced likelihood of catching all families of finfish investigated in dense krill aggregations, which is consistent with the literature. These predictive models were used to estimate bycatch rates per tonne of krill catch for Channichthyidae, Nototheniidae and Myctophidae to predict total finfish bycatch of the Area 48 Saga Sea krill fishery and quantify the impact of this bycatch on the finfish stocks (Peatman et al., 2012).

The report presents a methodology that can be used to estimate the total finfish bycatch of the Area 48 krill fishery and quantify the impact of bycatch on the finfish stocks using data that is currently available, applied to data collected by Scientific Observers on the FV Saga Sea.

Estimates of bycatch rates per tonne of catch were made at a family level for Channichthyidae, Nototheniidae and Myctophidae species using delta-lognormal GLMs presented in Martin et al. (2012).  The bycatch rates were applied to estimates of krill catch to estimate precautionary total bycatch numbers of each family for both low sea-ice and normal sea-ice years.  Biological parameters for Champsocephalus gunnari and Notothenia rossii were used to convert total bycatch numbers to biomass at age of 50% maturity removed from stocks due to finfish bycatch at a family level, for Channichthyidae and Nototheniidae species respectively.  Family specific biomass estimates at age of 50% maturity were then compared to available abundance estimates of Channichthyidae and Nototheniidae species at a family level, and at a species level for C. gunnari and N. rossii.

This methodology takes account of explanatory variables that are significantly associated with presence/absence of finfish bycatch in estimates of finfish bycatch rates in the Saga Sea krill fishery.  The methodology can also provide quantitative analysis of the impact of the krill fishery on finfish species at a family level, as well as for individual species.