Depredation is an increasing concern in areas where Patagonian toothfish is targeted. In order to assess its effect and understand the process of depredation by killer whales and sperm whales, good quality data needs to be collected at sea. This paper describes the different variables collected by observers within French EEZ (area 58.5.1 and 58.6).

Depredation is observed in many places within the convention area and is an increasing concern for fishery. Killer whale and sperm whales are the two odontocetes species mainly responsible for this issue. One of the key point to understand the process of depredation is to use photo-identification, pictures taken by observers on board longline vessels targeting toothfish have been analysed. Several photo-identification catalogues have been produced as a reference to identify individuals.

This photographic guide has been developed with the aim of assisting observers at sea in the correct identification of seabirds. It does this through providing several images for each species, at different stages and different angles both in the air and in the water. This project has been achieved thanks to French observers at sea working in Kerguelen and Crozet waters under the TAAF administration scheme.

Understanding and predicting the distribution of Antarctic toothfish (Dissostichus mawsoni) habitat suitability at different spatial and temporal scales can make an important contribution to CCAMLR’s management of this species. In this paper the use of Maxent models with fishery catch data and environmental data to provide a circumpolar prediction of Antarctic toothfish habitat suitability is explored. Maxent is highly sensitive to biased sampling effort and the selection of background data. Hence, the influence of background data selection on Maxent model predictions using two approaches was tested: (1) a random background sampling approach across the CAMLR Convention Area and; (2) a target-group background sampling approach that aims to correct biased sampling effort. The predictions from the target-group background approach resulted in the most accurate predictions, but predictions needed to be constrained within an appropriate bathymetric range via post-processing. This resulted in an accurate and more realistic prediction of the distribution of Antarctic toothfish habitat suitability.

Grenadiers (Macrourus spp.) are a common by-catch of the Australian fisheries in Division 58.5.2, particularly for longline operations targeting Patagonian toothfish (Dissostichus eleginoides) (Van Wijk & Williams 2003; CCAMLR 2014). Improved identification tools and targeted sampling during the 2015 random stratified trawl survey confirmed that Caml grenadier (Macrourus caml) is present in Division 58.5.2, and likely to one of the most common grenadier by-caught. Sufficient data was collected in the survey, and subsequent commercial longline operations, to enable an assessment of the long term sustainable yield of M. caml at HIMI using the Generalised Yield Model. On the basis of this information, we propose an increase in total allowable catch based on the long-term sustainable yield for M. caml, and a proportional readjustment of the move-on rule.

An updated and revised assessment of the Patagonian toothfish (Dissostichus eleginoides) stocks in Crozet areas is presented. This study is based on a CASAL (C++ Algorithmic Stock Assessment Laboratory) model. Different statistical and biological datasets are used: catches, tag releases/recaptures data and commercial catch-at-length data.

An updated assessment of the Patagonian toothfish (Dissostichus eleginoides) stocks in Kerguelen area is presented. This study is based on a CASAL (C++ Algorithmic Stock Assessment Laboratory) model. Different statistical and biological datasets are used: catches, tag releases/recaptures data, commercial catch-at-length, data from the “PoKer” surveys, and the last age readings.

Research into the early life stages of Pleuragramma antarctica is essential to understanding how oceanographic variation will impact spatial distributions over time. The recent findings of nursery grounds in Terra Nova Bay have led to added inquiry into larval distribution and life history traits in the Ross Sea. A report submitted to the CCAMLR-EMM working group last year provided abundance, length and growth data for larvae found in the western and eastern Ross Sea during the austral summer of 2013, which were identified as P. antarctica based on morphological characteristics. We extracted genomic DNA from a sample of these larvae and, using fish universal primers, amplified part of the 16S rDNA and the D-Loop region. Despite evidence of DNA degradation, sequencing was nevertheless successful in a fraction of samples. Sequences were aligned with known GenBank sequences for P. antarctica and several of related notothenioids, which confirmed the species identity of larvae in the western Ross Sea as P. antarctica. Consistent with the previous report, D-Loop sequences also demonstrated that recently hatched larvae sampled from the eastern Ross Sea were from the same species, suggesting the possibility of another nursery ground for P. antarctica in the vicinity of the Bay of Whales. This is a novel use of mitochondrial DNA to test morphological identification when examining spatial distributions of P. antarctica that depart from expectation

Model diagnostics have been  applied to CASAL-based assessments of toothfish populations (Ziegler et al., 2015; SC-CAMLR-XXXIV/05, Appendix D). Similar diagnostics can be applied to any integrated model. The purpose of this note is to use some of these methods to explore aspects of the assessment being developed to set catch limits for Antarctic krill in Subarea 48.1.

A "base-case" configuration of the krill model was presented to EMM in 2015 (Kinzey et al. 2015a) along with seven alternative configurations based on different data weightings. These alternative configurations were to evaluate the influence of different data sources on the model estimates. The work described here augments the previous description of the base case and differently-weighted configurations with a table of likelihood components for the configurations and additional diagnostic methods for the base case. An additional diagnostic based on using simulated data as a "self-test" of model consistency for the estimates of derived parameters will also be provided.