CCAMLR

As a means of testing the accuracy of observer identifications and to provide additional data on benthic invertebrate by-catch; observers operating aboard the longline fishing vessel Antarctic Chieftain were asked to identify and retain all benthic invertebrate by-catch from a recent voyage to Heard Island and McDonald Islands (HIMI) for onshore verification by taxonomists at the Australian Antarctic Division (AAD). Observers displayed a high level of accuracy (89%) in the identification of longline invertebrate by-catch. Common taxonomic errors included the misclassification of stylasterids, bryozoans and alyconarians; an issue prevalent in similar fisheries. This suggests the need for further training in the classification of cnidarians (stony corals, soft corals and stylasterids) and coral-like organisms (bryozoans), particularly as these groups are of high conservation value and are potentially vulnerable to the cumulative impacts of bottom fishing

Catch-at-age proportions can be estimated directly using a simple random sample (SRS) of fish in the catch, determining the ages in the sample, and calculating proportions in each age class. This “direct” method can be replaced by the age length key (ALK) method if there are in addition to the SRS of ages, which have also been measured for length (the age-length sample), a large SRS where fish are measured only for length and tallied by length bins (the LF sample). The ALK method uses the extra information in the LF sample to calculate proportions by age by multiplying the probability by length bin estimated from the LF sample by the conditional probability of age given length bin estimated from the age-length sample and summing over the bins. Since this conditional probability is employed, the ALK method can be used to obtain unbiased estimates from age-length samples that have been collected using unequal probability schemes with respect to length such as length bin random sampling (LBrs). LBrs can be easily implemented by directing observers to sample length-bins for fish to have their otoliths removed until a fixed number of fish in each length bin have been obtained and further sampling from any “full” bins is discontinued for the remainder of the cruise. LBrs ensures that the tails of the length frequency distribution, and thus that of the age distribution, are over-represented in the age-length sample compared to a SRS scheme. Formulae for the precision of the estimates of proportion by each age class are given for each of the direct, ALK_SRS, and ALK_LBrs sampling/estimation methods. Since the formulae depend on the values of proportion by length bin and conditional probability of age given length as well as sample sizes, typical values were generated to allow the comparison of precision. The ALK_SRS method always has a greater precision across all age classes than the direct method, with the difference diminishing as the sampling fraction of the LF sample that is sub-sampled for age determination increases. The ALK_LBrs method is slightly inferior to the other two sampling/estimation methods for most of the age classes that dominate the catch but is significantly superior for the less well represented age classes. For older, mature-age fish this improvement in precision, being important in estimation of spawning stock biomass, may more than compensate for the slight loss of precision for the younger age classes in terms of the accuracy of integrated assessments that employ catch-at-age proportions.

Integrated assessments that use catch-at-age or abundance-at-age data for model calibration require an ageing error matrix as input in order to adequately account for uncertainty in the data resulting from the imprecision of age determination using annual ring counts from otoliths. This paper describes the methods and results used to provide an ageing error matrix to the HIMI toothfish integrated assessment using repeat readings by 4 readers of a set of 203 reference otoliths sampled from the HIMI fishery. The methods of sampling, preparing, reading, and modelling random reader error for this reference set of otoliths is described. A total of 933 readings were taken and errors were defined as the nearest integer (NI) value deviations, denoted as integer errors (IE), from the mean age for an individual otolith. Since the true age of the fish is unknown, only imprecision and relative differences between readers could be quantified. Linear mixed model analyses indicated that the mean IE ranged between readers only slightly (+/- 0.27 yr) whereas frequencies of random IEs, treated as classes, between readings were relatively high for +/-1 yr relative to the zero IE frequency, and less so for the +/-2 yr and greater classes. These frequencies depend on the readability score of the otolith and its average age and were modelled in two stages. In the first stage the frequency of the absolute value of IE, the AIE, considered as 0, 1, 2, 3, 4, 5 yr and greater, classes for each of the 4 readability classes and 7 aggregate age classes were modelled using continuation ratios and predicted proportions in each AIE class obtained for a given readability score and age. Proportions of the AIE 1 yr error class decreased relative to the AIE 0 class as readability improved while, in general, it increased as age increased. To model any degree of asymmetry in IEs, a binomial/logistic model of the proportion of non-zero IEs that were negative was fitted for given readability and age. This probability decreased from around 0.6 to 0.3 for ages 5 and 21 yr respectively, but did not depend on readability. The construction of the ageing error matrix is described and combines the modelled probabilities for AIE and negative IE while taking into account logical constraints. This two stage approach makes efficient use of the data since only half the number of combinations of error class by readability by age class are required compared to modelling IE classes directly. This approach differs from other studies of ageing error in that it takes into account the otolith readability score and the integer nature of ring count data.

The integrated assessment of Patagonian toothfish, Dissostichus eleginoides, for the Heard and McDonald Islands (Division 58.5.2) was updated by replacing catch-at-length proportions from commercial catches with catch-at-age proportions by applying age length keys (ALKs) to gear- type/ground (i.e. sub-fishery) and year-specific length frequency (LF) data. A Poisson log-linear (contingency-type) analysis of the age-length frequency data suggested that ALKs could be pooled across sub-fisheries without significant loss of information. Pooling the ALKs in this way meant that all commercial catch data could be input to CASAL as catch-at-age proportions except for the 2009 catches. For 2006 and 2007 random stratified trawl surveys, ALKs were used to convert abundance-at-length to abundance-at-age data. Effective sample sizes for the commercial catch-at-age proportions, assuming a multinomial distribution, and the coefficient of variation (CV) for the abundance-at-age, assuming a lognormal distribution, each took into account uncertainty due to haul-level variability in catch-at-length proportions, ALK sampling error and random ageing error. CASAL allows a single ageing error matrix to be defined and applies this matrix to predictions of numbers-at-age and proportions-at-age. In other work, this matrix was found to depend on the readability score of the otoliths used for ageing, so the ageing error matrix was calculated as a weighted average of elements across nearest integer average readability scores for sub-fishery by year aged otoliths where the weights were the effective sample size for proportions-at-age. Compared to the assessment that did not incorporate catch-at-age or abundance-at-age data, the aged-based assessment dramatically lowered the CV for the recruitment series, from around 1.8 down to approximately 0.6. The other major differences to the a2-ess model of Candy and Constable (2007, CCAMLR Science 15, 1-34) were that catches and catch-at-age data for additional sub-fisheries (2 longline, 1 trawl) and pot fishing in the 2006 season were included since catches in these cases have become significant in recent years, and the CV of mean length-at-age was fixed at 0.1 and not estimated since the inclusion of age data has reduced the ability to successfully estimate this parameter. The long-term yield that satisfies the CCAMLR decision rules, using a split of the catch by sub-fishery corresponding to actual catches in 2008, was 2 565 t.

A random depth stratified bottom trawl survey of South Orkney Islands (CCAMLR Subarea 48.2) demersal finfish was completed during February-March, 2009 during the U.S. AMLR Program field season. Detailed information is presented for several of the most abundant demersal finfish species, including spatial distributions, size compositions, diet compositions, gonad development, and standing stock biomass within the 500 m isobath of South Orkney Islands. Spatial distribution of standardized finfish densities demonstrated substantial contrast across the shelf area, with most finfish biomass occurring on the northwest sector of the shelf area. The highest densities of demersal finfish occurred at stations north of Inaccessible and Coronation Islands, and the highest mean densities occurred within the 150-250 m depth stratum. Estimates of biomass for nine abundant species were generally low. Standing stock biomass of Champsocephalus gunnari, the primary species targeted by the commercial fishery from the late 1970’s through the 1980’s, has increased since the last survey conducted in 1999, though remains severely depressed, and has not recovered to pre-exploitation levels.

1. A three-year tagging study in the South of Subarea 48.4 was initiated in the 2008/09 fishing season with the aims of providing the data required for assessments of the population structure, size, movement and growth of both Dissostichus eleginoides and Dissostichus mawsoni in the South of 48.4; 2. In 2008/09 a total of 214 D. eleginoides and 192 D. mawsoni were tagged and released. Two tagged D. mawsoni were recaptured in the first season. 3. Macrourids and rajids dominated finfish bycatch with discard weights at 19% and 0.8% of toothfish catches, respectively. These rates are similar to those experienced in the North of 48.4. 196 Amblyraja georgiana and 1 Bathyraja eatonii were tagged and released and none were recaptured; 4. Almost half (47%) of sets were undertaken during daylight hours and no bird entanglements or mortalities were reported; 5. The UK proposes to continue the mark-recapture experiment in the South of Subarea 48.4 in 2009/10 with a combined catch limit of 75 tonnes for Dissostichus spp. 6. It is proposed that tagging effort is restricted to D. mawsoni which dominates catches in the South of 48.4.

1. The bycatch TAC of 12 tonnes of macrourids was reached in 2008/09 triggering the early closure of the fishery with only 58.9 t of the 75 t TAC of D. eleginoides taken; 2. The target tagging rate of 5 fish per tonne was exceeded by both vessels, with 344 D. eleginoides tagged and released in 2008/09, slightly under the target of 375 for the full 75 tonnes of catch; 3. There were 29 D. eleginoides tag returns in 2008/09, bringing the total from all seasons up to 54 tag recaptures; 4. A total of 12 D. mawsoni and 269 rajids were tagged in all seasons combined though none were recaptured; 5. Evidence of gonad development in 2008/09 suggests that D. eleginoides may spawn in the North of 48.4 in some years; 6. Analysis of tagging and biometric data suggests that D. eleginoides at 48.4 have similar growth parameters to those of 48.3; though the bulk of the current vulnerable biomass may be composed of a limited number of cohorts. 7. CASAL confirms that the vulnerable biomass is dominated by a single cohort spawned in the early-1990s and that growth parameters are similar for D. eleginoides caught in 48.4 and 48.3. 8. CASAL estimates of D. eleginoides population size in the North of 48.4 agree with simple Petersen and local CPUE estimates, with a B0 of 1143 t and B2009/B0 of 85%. 9. The sustainable yield calculated according to CCAMLR decision rules is 47 tonnes.

Observer data collected on longliners between 2003 and 2009 was analysed to look at the levels of depredation caused by killer whales (Orcinus orca) and sperm whales (Physeter macrocaphalus) around South Georgia. Since 2003 cetaceans have been observed on 3000 out of 14644 observed lines (20.5%) with killer whales present on 562 lines (3.8%) and sperm whales on 2588 lines (17.7%). By comparing the catch per unit efforts (CPUEs) with and without the presence if cetaceans, the amounts lost were also examined and varied from 0.9% to 5.8% of the TAC per year over the time period. Additional work was also conducted which looked at the behaviour of cetaceans around fishing vessels in the area. To aid identification and give an idea of population size, a photographic catalogue of individual animals was developed. To date, a total of 35 killer whales and 65 sperm whales have been seen, with re-sightings occurring both in season and between years. Hydrophones deployed on longlines recorded both vocalisations of cetaceans and noises made by the vessels, generated by propeller cavitations, gearing and drive shafts, the hydraulics of line machinery and the fishing gear itself. Distinctive acoustic patterns were seen both when winches were engaged and when the drive on the vessel was engaged during hauling. Recordings made from a distance of 3.4nm away were picked up by the hydrophones and it is thought that these acoustic cues could be responsible for attracting the cetaceans.

Stomach contents were identified from 206 Antarctic skate (Amblyraja georgiana) that were collected during three groundfish surveys (winter 2007, 2008 and summer 2009) around the island of South Georgia, Southern Ocean. The diet of A. georgiana varied with skate size and between years. Preferred prey included fish (particularly for larger individuals) and Antarctic krill, as well as amphipods, polychaetes and other benthic fauna. The Antarctic skate appears to be an opportunistic predator and the clear presence of Antarctic krill in this skate’s diet indicates a benthic habit of this euphausiid species, which has hitherto been considered as occupying a purely pelagic niche.

An attempt to use reported catch and CPUE data based on data of observers in the assessment of Antarctic toothfish stock in Division 58.4.1 is made. Unlike some previous results obtained for this division (e.g. Agnew et al., 2008), the assessment was intended to be independent of estimates of toothfish stock biomass in the Ross Sea. As a result of implementation of an age-structured and a dynamic production models, current stock biomass in this division is estimated as about 12000 t, while the estimate of carrying capacity for this stock is found to be about 29000 t. Initial stock biomass is estimated as 19000 t.