CCAMLR

The Spanish vessel Tronio started the research plan in the 2012/13 season using the Spanish bottom longline system. One depletion experiments was completed in each of the SSRU surveyed (58.4.1H and 58.4.1G). Three prospecting-phase clusters of sets did not reach the established threshold to start the depletion.

A prospective estimation of the local biomass (B_LOC) of the two localized areas where the depletion experiments were performed is done as well as an estimation of the biomass of the SSRUs (B_SSRU), maximal and minimal, considering areas with high and low densities.
 
A summary of the activities and results from the survey is also presented related to the sampling scheme, collected samples and species involved.

Research and monitoring (RM) plans need to be developed for marine protected areas in an interdisciplinary way which includes scientific knowledge, ongoing and future collaborative projects and management expertise.  In 2012, the Working Group on Ecosystem Monitoring and Management considered a number of issues relating to the development of RM plans, including that guidance is needed from the Scientific Committee and Commission in the detailed structure of the plans.  SC-CAMLR endorsed advice from WG-EMM that RM Plans needed to relate to the requirements for an MPA and that the research and monitoring should be achievable in practice. The proposal for the East Antarctica Representative System of Marine Protected Areas (EARSMPA) contains the priority elements of the RM Plan to support the management of that system.  Here, we show how the scientific initiatives currently underway in the region could be used as a foundation for a RM Plan for the EARSMPA.  All Members are invited to participate in the research and monitoring activities, particularly through these initiatives.  This paper is structured to first provide the text from the draft conservation measure for the EARSMPA on the priority elements of the RM Plan.  Second, summaries are provided of initiatives currently underway that could provide research and monitoring in the region.  Last, we assess which parts of the RM Plan would benefit from the outcomes of the initiatives.

As part of the Secretariat task to ensure the quality and consistency of the data arising from the CCAMLR SISO, an algorithm was designed to automatically import and check data entered by observers in their logbooks. A first version of the algorithm was tested on four randomly selected longline fishery logbooks. In its current version the algorithm is able to detect and report invalid data formats, as well as value inconsistencies through a limited set of logical tests. For each logbook inspected, a text report indicating the locations of faulty entries, a set of figures to display length and weight of toothfish, and, an overall data entry quality score, are automatically generated. The first results highlighted some recurring issues, such as the addition of rows or columns of data within worksheets, which displaces data and results in the importation of the wrong data by the algorithm and invalidates automated data processing routines.

Proposed Research plan for the survey of Ukrainian longliner in 48.2 subarea.

The Scientific Committee has provided detailed advice on the preparation and evaluation of research plans in data poor fisheries submitted under CMs 21-02 and 24-01 in 2011 and in 2012. We formulate advice arising from extensive Working Group discussions and highlight additional design and implementation issues raised during research plan design and evaluations. Our aim is to streamline the review process, promote quality research designs, and make the proposed catch limits more explicit with regard to the inherent trade-off between quickly developing quality stock assessment input data and potential biological risks arising from uncertainty in the research implementation process.

Toothfish stock assessment results are strongly influenced by tag-release and tag-recapture data, and rely on the assumption that tagged and untagged fish have constant probabilities of recapture regardless of the spatial distribution of releases or subsequent fishing effort for recaptures. Conceptually this assumption implies either that tagged and untagged fish mix equally in the population, or that fishing effort for recaptures is distributed in proportion to the underlying abundance. Neither of these conditions are likely to occur in practice, and violation of this assumption may lead to bias. In this paper we investigate such potential biases in the assessment of Antarctic toothfish in the Ross Sea fishery using simulated outputs from spatially explicit operating models.

Two spatially explicit operating models were developed: each was comprised of 189 discrete cells, with a cell size of about 25,000 km². The first model was restricted to those locations of the Ross Sea region that have been fished (restricted model), and the second model extended to encompass all areas (unrestricted model). Simulated observations were generated from these models, and used as inputs into a simplified non-spatial stock model based on the 2011 Ross Sea toothfish stock assessment.

Results suggested that the assessment model was biased low by 17% or 43% assuming movements defined by the restricted and unrestricted models respectively. The bias was thought to reflect the underlying distributions of tag-releases and subsequent fishing effort, and the limited mixing of fish between areas — more than half of tags have been released (and subsequently recaptured) from SSRUs 88.1H and 88.1I, while a large proportion of the fish are in remaining SSRUs where fewer tags were released and with lower fishing effort. This effect is accentuated in the unrestricted model, where about half of the fish are distributed in areas that had not been subject to fishing effort.

We note that the extent of bias will depend on both the proportion of fish in unfished areas and movement rates between fished and unfished areas, but that misspecification of other parameters in the assessment models (for example tag mortality rates and tag detection rates) or alternate spatial hypotheses may also introduce biases that we have not considered in this paper. While additional analyses need to be undertaken to confirm or improve the spatial models used here and alternative movement hypotheses should be tested, we consider that these simulation experiments provide a useful tool to evaluate potential bias and uncertainty in our understanding of the assessment in the Ross Sea toothfish stock and potentially similar tag-based assessments elsewhere in the CCAMLR Area. They are also useful in investigate the likely consequences of management strategies for stock assessments, including changes in fishing effort or tagging distributions. They can also be used to investigate the potential effects of alternative biological hypotheses for less well defined parameters, for example maturity and natural mortality rates.
 

We present an optimised spatially explicit age-structured population dynamics operating model for Antarctic toothfish over the entire Ross Sea region, for a medium scale spatial resolution (189 spatial cells) covering the Ross Sea region.

The model was developed as a generalised Bayesian population dynamics model implemented using the Spatial Population Model software (SPM), and a single sex age-structured model that categorised fish as immature, mature, pre-spawning, spawning, or post-spawning. Observations include spatially explicit commercial catch proportions-at-age, proportions mature and proportions spawning (based on GSI data), CPUE, and tag-release and tag-recapture observations. Estimates of parameters appeared to broadly reflect the hypothesised spatial distribution of Antarctic toothfish, suggesting that younger fish were found predominantly in southern areas of the Ross Sea shelf, mature fish on the slope and spawning fish in the northern areas of the Ross Sea region. Fits to the observations were generally good.

Further investigations are recommended in order to further validate and refine this model. These include testing alternative model assumptions around fishing selectivity, maturity and migrations;  as well as collecting further data to better inform the maturity ogive and spawning migration. We also recommend a survey of the assumed spawning grounds during winter, and the controlled collection of data in areas not fished to date. In addition, further external validation diagnostics need to be developed for this model, and also for any other such models being developed. Due to the complexity of the problems encountered in validating spatial models, a spatial modelling workshop would be of great benefit.

Although still undergoing development, this model is useful for investigating the direction of potential biases in the current single-area stock assessment model, and for carrying out management strategy evaluations under various alternative hypotheses of fish movement.

Tag release and recapture data are used in integrated age-structured stock assessments of Antarctic toothfish (Dissostichus mawsoni) in Subareas 88.1 and 88.2 to determine abundance and sustainable yields. The assessment model assumes that all vessels have equal tag detection rates; as a consequence including observations from vessels with low detection rates (and/or low tagging survival rates) could lead to an over-estimate of the stock biomass.

In this paper we develop further an index of vessel-specific tag detection performance for the Ross Sea fishery using a case-control methodology which controls for the inter-annual spatial and temporal variability of commercial fishing operations from which tags are released and recaptured. We then develop selection criteria, which can be used to determine the subset of vessels for which  there is confidence in their tag-recapture data. Finally, we apply these selection criteria to the tag data available at the time of the 2011 stock assessment to determine the effect of this selection method on the tag data available and also illustrate its effect on the results of that assessment.

For each vessel in the fishery, every fishing haul from the nominated ‘case’ vessel was matched to one or more ‘control’ hauls from other vessels fishing in the same time and location (i.e. in the same fishing season and within a specified distance).  The index was calculated as the scaled number of tags recaptured by the case hauls relative to the number of tags recaptured by the matched control hauls. By iterating over all events for all vessels, we generated a relative index of tag detection rate for each vessel, with a value of one representing the average performance across all vessels.

The tag data from vessels for which the confidence interval was greater than zero and extended at or above one were selected for inclusion in the stock assessment. When applying this decision rule, 75% of all tags released and 83% of tags recaptured were selected for inclusion in the analysis. In comparison, when the 2011 stock assessment was carried out using a different data selection algorithm, more than 90% of all tags released and recaptured were selected for inclusion.

We recommend this approach be used as a data selection method for the stock assessment of toothfish in the Ross Sea region. The application of such indices could be extended to other regions where tag data is used in stock assessment, and developed for other data types such as catch per unit effort when used to inform stock assessment.

In the Division 58.4.1 there are two stocks of Dissostichus mawsoni; one extends from the SSRU 58.4.1C to the SSRU 58.4.2A, and the other one to the SSRU 58.4.1H. The population sizes were vulnerable with a big range of about 1,000-2,000 t per SSRU in 58.4.1. However, there is not enough data and information to assess the stocks and deliberate proper measurements for sustainable utilize such as for precautionary catch limits. Therefore, the Korean scientists and fishing company (Insung Co.) submitted cooperatively the research plan for the exploratory longline fishery for Dissostichus spp. in SSRUs of 58.4.1 C and E using the fishing vessel, No. 3 Insung belonged to Insung Corporation during the CCAMLR meetings in 2012 to collect the fishing, biological and ecosystem data for the robust stock assessment.  

The scientific research plan was carried out barely only in SSRU 58.4.1C-a and C-b during 2012/2013 season because of the harsh sea condition, even though the plan was set to conduct in 58.4.1 C-a and C-b, and E-a and E-b.  The research vessel arrived in SSRU 58.4.1E-b and E-a, but we could not set even a fishing gear due to the very bad ice condition. In SSRU 58.4.1 C-b, we made five sets of each fishing gear of trotline and Spanish line, and caught only two D. mawsoni by trot line during 19-21, February. In SSRU 58.4.1C-a, we caught 30 D. mawsoni by five sets of Spanish line and 56 D. mawsoni by six sets of trot line. We could not make even sets of the two fishing gears because of the limit fishing area to operate due to the severe sea condition. The CPUE of D. mawsoni was 0.077 kg/hook and 0.144 kg/hook for Spanish line and trot line, respectively. However, it was not enough data to compare catch rate between Spanish line and trot line. The highest CPUE by fishing depth was 1,500-1,700 m. The CPUE of the fish by Pacific herring bait was higher than that for humboldt squid bait. The tagging rate was over 9.47 fishes per tonnage of green weight, and the tagging overlap was 82.3%. The average length for the male was 128.4 cm, and the female was 136.1 cm. The equation of relationship between weight and length for the male was calculated, as W = 0.000004L^3.244, and for the female W = 0.000003L^3.299. The dominant prey item was fishes composing of 88.67% of frequency of occurrence with 65.00% by the numbers and 88.67% by the weight, and 90.23% of IRI in the diet. The female ratio in the 84 fishes was 0.54 displaying the tendency increased with the total length became larger. The gonadosomatic index of D. mawsoni was 2.6 for the female and 1.1 for the male. The most of females and males were in maturing stage. Based on the results driven by the histological methods the spawning season was presumed from May to June, and D. mawsoni may spawn two or three times in a spawning period in SSRU 58.4.1C-a. To make clear all of the results mentioned above, reanalysis should be carried out with more data, information and samples.