CCAMLR

The work of the Scientific Committee is expanding with a duplication of some functions in different working groups. There is also a need to give time to emerging issues for which there is insufficient time at present in the existing working groups, such as consideration of marine protected areas in a CCAMLR context. It is proposed that the working groups of the Scientific Committee be revised to streamline the work and to help reduce the number of groups needing to be attended by our experts. The Working Groups are proposed to be:
i) Biology, ecology and conservation – to discuss the broad issues and ideas about how the Antarctic marine ecosystem works and general conservation requirements, including the use of marine protected areas in the CCAMLR context,
ii) Development of assessment methods – to develop methods for (a) assessing fish, krill and bycatch populations, (b) status of predator and other populations and habitats, (c) ecosystem monitoring, and (d) estimation of yield as well as (e) methods for evaluating management systems, and
iii) Assessments – to use approved and evaluated methods to assess (a) fish, krill and bycatch populations, (b) status of predator and other populations and habitats, (c) status of the ecosystem, and (d) yield.
The ad hoc Working Group on Incidental Mortality arising from Fishing could be spread across all three with a more dedicated report in the Assessment working group as is the current practice.

The history of human harvests of seals, whales, fish and krill in the Antarctic is summarized briefly, and the central role played by krill emphasized. The background to the hypothesis of a krill surplus in the mid 20th Century is described, and the information on population and trend levels that has become available since the postulate was first advanced is discussed. The objective of the study is to determine whether predator-prey interactions alone can broadly explain observed population trends without the need for recourse to environmental change hypotheses. A model is developed including krill, four baleen whale (blue, fin, humpback and minke) and two seal (Antarctic fur and crabeater) species. The model commences in 1780 (the onset of fur seal harvests) and distinguishes the Atlantic/Indian and Pacific sectors in view of the much larger past harvests in former. A reference case and five sensitivities are fit to available data on predator abundances and trends, and the plausibility of the results and the assumptions on which they are based is discussed, together with suggested further areas for investigation. Amongst the key inferences of the study are that: i) species interaction effects alone can explain observed predator abundance trends, though not without some difficulty; ii) it is necessary to consider other species in addition to baleen whales and krill only to explain observed trends, with crabeater seals seemingly playing an important role and constituting a particular priority for improved abundance and trend information; iii) the Atlantic/Indian region shows major changes in species abundances, in contrast to the Pacific which is much more stable; iv) baleen whales have to be able to achieve relatively high growth rates to explain observed trends; and v) Laws’ (1977) estimate of some 150 million tons for the krill surplus may be appreciably too high as a result of his calculations omitting consideration of density dependent effects in feeding rates.

An ecosystem, productivity, ocean, climate (EPOC) model has been developed in the R statistical language to help explore topical issues on Antarctic marine ecosystems, including impacts of climate change, consequences of over-exploitation, conservation requirements of recovery and interacting species, and the need to evaluate whether harvest strategies are ecologically sustainable. As such, it can be used to facilitate the development of plausible ecosystem models for evaluating management procedures for krill following the recommendations of the workshop held by WG-EMM in 2004. EPOC has been designed as an object-oriented framework currently built around the following modules: (i) Biota, (ii) Environment, (iii) Human activities, (iv) Management, (v) Outputs, and (vi) Presentation, statistics and visualisation. Each element within a module is an object carrying all its own functions and data. EPOC is designed to be a fully flexible plug-and-play modelling framework. This is because of the need to easily explore the consequences of uncertainty in model structures but, more importantly, to enable ecosystem modelling to proceed despite widely varying knowledge on different parts of the ecosystem and avoiding the need to guess model parameters for which no information exists. EPOC provides these opportunities as well as examining the sensitivity of outcomes to changes in model structures, not only in the magnitude of parameters but in the spatial, temporal and functional structure of the system. A case study for Antarctic krill is presented as an example.

Wide-scale monitoring of the status of Antarctic krill resources had been previously conducted by the USSR and its results still allow scientist to prepare forecasts of changes in the ecosystem. However, at present such monitoring seems difficult. Taking into account these points, Ukrainian scientists requested that draft amendments to a number of CCAMLR Conservation Measures be considered to make the System of International Scientific Observation compulsory in Antarctic krill fisheries. Indeed, a widespread introduction of scientific observation on krill fishing vessels can help to ease the existing deficit of fishery-independent krill surveys.

Preliminary analysis of data from the questionnaires of krill fishery behavior in CCAMLR observer manual was undertaken. The analysis revealed possible inconsistency in definitions of the event codes among different skippers. Some suggestions were made to improve the quality of questionnaire format to better understand the nature of the fishery.

The behaviour patterns of Japanese krill fishery vessels in Area 48 were analysed using questionnaires on the reasons why the vessel changed their fishing grounds, which were sent out to of the Japanese fishing vessel since the 1989/90 fishing season. Among many reasons for changing fishing grounds, krill density, krill, size, ice condition, transshipment, and salp-by catch accounted for 95.6% of the changes. Although low krill density was the primary reason for changing fishing grounds, other seasonal factors such as greenness or ice condition could become important. A general picture of the seasonal succession of the Japanese krill fishing operation revealed that they tend to utilize fishing grounds close towards the southern limit within the ice free range. This pattern may well vary between nations, and it is essential to perform similar analyses for the other nation’s vessels. A conceptual model for Japanese krill fishing operation is proposed.

Growth trends of Antarctic krill with sex, length, season and region using over 10 years accumulation of instantaneous growth rate (IGR) measurements were modelled using a Linear Mixed Model (LMM). A model of inter-moult period (IMP) as a function of temperature, required to convert IGR to specific growth rate, was fitted to data from published constant-temperature rearing studies and this model was used to predict seasonal IMP using a model of the average sea-surface temperature seasonal trend for each region. Smaller krill exhibited higher growth rates and a progressive decrease in the IGR with increasing size was generally observed. This trend decreased from summer to autumn with small to negative values of IGR predominating across all size classes by autumn. The period of rapid growth was December in Indian sector, whereas in the Scotia Sea sector it appeared to be a few months earlier than this. Significantly lower growth rates were exhibited by females in January and February relative to males. Seasonal specific growth rates estimated in this study were compared to previous studies, and suggested that wild krill show more rapid growth over a shorter growth period than it was traditionally thought.

Patterns of fishing ground selection were characterized using STATLANT and CCAMLR fine scale data. Among the 15 SSMUs within Areas 48.1, 48.2 and 48.3 including the pelagic SSMUs, only one third of them were identified as the main contributors to the total catch. A shift of operational timing towards later months within fishing seasons was observed in Area 48.1 (Dec-Feb to Mar-May). However, operational timing stayed relatively constant in Areas 48.2 (Mar-May) and 48.3 (Jun-Aug). During a quarter century of krill operation in Area 48, patterns of SSMU usage has changed. Three different patterns of seasonal SSMU selection were characterized by following the result of cluster analysis. Frequently used SSMUs did not always match the areas of high krill densities observed by scientific surveys, and possible reasons for this mismatch were further discussed. Desire for revised data submission format was also recognized in order to accommodate any possible development of the fishing techniques.

Von Bertalanffy (VB) growth models for Antarctic krill have in the past been calibrated from population-level data consisting of modal lengths obtained from a time sequence of length frequency samples. We develop an alternative approach to predicting the trajectory of length over time using a step-growth function that combines models of instantaneous growth rate (IGR) at moult calibrated from direct measurements of individual pre- and post-moult krill sampled from the wild with a model of temperature-dependent intermoult period. Using summer and early autumn data for juveniles and males sampled from the Indian Ocean sector we model IGR as a function of pre-moult length and season using linear mixed models incorporating cubic smoothing splines. We generate a number of growth trajectories starting from an age 1+ mean length for different scenarios of winter and spring growth. We then provide convenient parametric approximations to these step trajectories using either punctuated-growth or seasonal-growth VB models. Our models indicate that, allowing for shrinkage, age 6+ mean length for the Indian Ocean sector was close to 53 mm compared to 57 mm obtained from studies for the Atlantic Ocean sector.

Krill carapaces measurements have been used to reconstruct krill length frequencies in Antarctic fur seal diet. The discriminant function currently used to determine sex, and the sex-specific allometric equations for calculating total length from carapace length, were derived from South Georgia krill populations. The equations have been applied to fur seal diet studies in the South Shetlands but until now have not been validated using locally sampled krill. This study reports on a three year study validating the use of discriminant functions to determine sex of krill based on carapace length and width and independently derives sex-specific regression models for krill collected in the South Shetlands. Allometric equations derived from South Georgia krill overestimated total length. Applying a discriminant function derived from mature krill in years following significant recruitment events with large proportions of immature krill resulted in significant bias towards male krill and an overestimation of krill length. We propose some standard guidelines for applying discriminant functions, allometric equations and for interpreting results.