CCAMLR

This paper describes French fishery targeting Patagonian Toothfish inside CCAMLR area, what are the data collected and how they are checked, a short description of prioritisation of observer’s task is given.

SeaBird is a generalised age- and/or stage-structured seabird population dynamics model that allows a great deal of flexibility in specifying the population dynamics, parameter estimation, and model outputs. The manual provides information on how to use SeaBird, including how to run it, how to set up the input files, descriptions of the population dynamics and estimation methods, and how to generate outputs. It also contains a brief overview of the technical specifications of the software, and examples of models using SeaBird. SeaBird is designed for flexibility. It allows the user to structure the modelled population in the way that best suits the available data. Depending on these data the user may which to specify the population structure using some or all of the following characteristics: age, life stage (e.g., immature or mature), sex, or behaviour (e.g., in any year mature birds may be classified as breeders or non-breeders). Interactions with fisheries can be modelled and the user can choose the sequence of events in a model year. A wide variety of types of data can be used. Estimation can be by maximum likelihood or Bayesian. As well as generating point estimates of the parameters of interest, SeaBird can calculate likelihood or posterior profiles and can generate Bayesian posterior distributions using Monte Carlo Markov Chain methods. SeaBird can project population status into the future under various alternative scenarios. SeaBird was designed to share many features and concepts with the fishery stock assessment model CASAL (Bull et al. 2005) and users of the latter program will find it easy to adapt to SeaBird. However, there are some important differences between the programs that are described.

ic life is developed in accordance with the rules of metrology, mathematical statistics and using the biocenological regularities of water objects. The basis of evaluating the stocks of water life was investigated through biocenotic conditionality in the areas of their equal probability in concentrations. The traditional method of squares in evaluating the stocks of water life was modernized with the use of probabilistic approach, the required knowledge of rules of statistical distributions of its specific concentration. Allocation of borders in the areas of probably equal concentrations of aquatic life applied in the proposed technique offer the mean integral values of probabilities. It is recommended to minimize errors of evaluation of the aquatic life resources using the statistical method of producing the average values if it is stated that casual component is more than two times higher of the regular component in a resulting error of evaluation of the average specific concentration of water life.

Quantitative method for describing krill mass congestions based on perennial observations using trawling and hydroacoustic data is proposed. Reliable evaluation of krill resources is provided with probability methods and spatial analyses requiring knowledge of statistical distribution rules applicable for natural habitat and equal probability concentrations. Spatial analyses of krill population densities have shown mixed rules of statistical distribution over its natural habitat. Data analyses procedures applied for trawling and hydro-acoustic sampling have to in compliance with the rules of statistical distribution and principles of metrology. Division of natural habitat of the Antarctic krill into regions is based on a principle of equal probability applied for congestions instead of the principle of equal proportions. Metrological features of evaluating population densities of krill are revealed. Standard measures for the population densities of krill in natural habitat are not available, thus it is principally impossible to structure systems and random errors in evaluating process, to define their impact on final evaluation of the resource. Special metrological principles are required and proposed for the correct evaluations based on reproducibility of statistic distribution parameters for krill and minimizing miscalculations in evaluations of congestions. Evaluations regime applied for the population density of krill should be defined by reliability and admissible error of estimated resource. Traditional concepts of observation system concerning the resources of krill should be further developed with applications of rules and parameters dealing with statistical distributions of population density, information about sources of errors, tools and methods of evaluation, standard techniques of minimizing errors in evaluations taking into account biological features of krill development. Methodical standards and uniform evaluation criteria for parameter evaluations of statistical distributions should be proposed to the Countries participating in the Antarctic Treaty as proceedings regarding minimal errors in the evaluation procedure. Reliable evaluation of krill resources requires advanced technical tools and observation systems. Population density values for krill should be calculated using Aitchison delta distribution. Primary statements regarding the krill in strategic planning for fisheries should include relevant biologically valid evaluations of population numbers for krill with the maintained reliability requirements and reasonable errors in evaluating the density of populations.

An updated version of the Spatial Multi-species Operating Model (SMOM) of krill-predator-fishery dynamics is described. This has been developed in response to requests for scientific advice regarding the subdivision of the precautionary catch limit for krill among 15 small-scale management units (SSMUs) in the Scotia Sea, to reduce the potential impact of fishing on land-based predators. The model includes krill as prey and four predator groups (penguins, seals, fish and whales) in each of 15 SSMUs. A number of updates have been made to the model such as linking krill growth rate to sea surface temperature, and these are described here. Moreover, the methodology used to condition the model using the WG-SAM set of reference observations for Area 48 (the SAM calendar) is described. Alternative combinations of model parameters essentially try to bound the uncertainty in, for example, the choice of survival rate estimates as well as the functional relationships between predators and prey. An example is given of how this Operating Model can be used to develop a management scheme which includes feedback through management control rules.

In this paper, we develop an ecosystem-based, precautionary management procedure for krill fisheries which draws together past experience in CCAMLR. It provides an empirical ecosystem assessment model, a decision rule for determining local scale catch limits based on a harvest strategy and a single-species assessment of yield, and a method for implementing the procedure. The decision rule for setting catch limits for a given harvest strategy has a straight forward expression of the target conditions to be achieved and the uncertainties that need to be managed and does not assume an understanding of predator-prey dynamics beyond that evident in the data. It is a natural extension of the current precautionary approach of CCAMLR for krill and can utilise existing datasets, including B0 surveys, local scale monitoring of krill densities, local-scale monitoring of predator performance, monitoring of predator foraging locations and time series of catches from the fishery. This procedure provides a common framework for inserting data, assessment methods and candidate modelling approaches for assessing yield. Consequently, its formalism means there is no need to undertake a staged approach in providing advice. The advice can be updated as improvements are made in any component of the procedure, including the provision of data, implementation of new assessment or projection models or a revision of the decision rule. This framework formalises the decisions that need to be made in dealing with an ensemble of food web models for providing suitably precautionary advice on how to spatially structure krill fisheries to account for the needs of predators. It provides the primary expectation for managing uncertainty, either by obtaining better estimates of parameters for the projection models and/or by altering the harvest strategy. Consequently, a preferred harvest strategy, which is initially untenable because of the uncertainties associated with its ecosystem impacts, could become a suitable option if its related uncertainties are reduced. Conceivably, the procedure outlined here could be used in a spatially-structured feedback management system that can ensure CCAMLR is able to respond to trends in the status of the ecosystem, including trends arising from climate change.

This paper details how FOOSA, an operating foodweb model for evaluating spatially-structured harvest strategies for krill, has been implemented within the EPOC modelling framework. It also shows how the parameters developed for use in FOOSA can be adapted for use in EPOC. The paper has three main parts – an outline of the structure of EPOC, consideration of the general FOOSA structure that needs to be implemented and a description of the implementation of FOOSA in EPOC. The latter section includes the methods used for implementing environmental variability, the krill population, generic predators, the krill fishery and the system for setting catch limits. The process of implementing FOOSA in EPOC has been a useful opportunity to consider the functions needed to represent different processes in a minimal realistic model. A number of revised functions are developed as options to reflect different dynamics that may be present in the krill-predator-fishery system in Area 48. Some of these functions and model structures have been generalised to enable more predators to be included in the food web and to provide flexibility in the number of stages of a predator consuming krill. An important step now in the implementation of FOOSA in EPOC is for this implementation to be reviewed by the developers of FOOSA.