Aller au contenu principal

    Defining fishing grounds in the Scotia Sea

    Demander un document de réunion
    Numéro du document:
    WG-EMM-02/40 Rev. 1
    Auteur(s):
    I.R. Ball and A.J. Constable (Australia), S. Kawaguchi (Japan) and D. Ramm (CCAMLR Secretariat)
    Point(s) de l'ordre du jour
    Résumé

    This paper provides a method for delineating krill fishing grounds in Area 48 based on commercial catch data for the region held in the CCAMLR database. It also summarises available information on krill distribution and abundance and movement for the region, which can be used to help understand the relationship between the fishing grounds and the krill population. We define a fishing ground as being a predictable location where the fishery obtains relatively reliable catches from one year to the next over a number of years. The quantity of interest is not only the total catch obtained from a location, say a 10 x 10 nmile area, over the years but how important that location is to the fishery each year, which is judged by that location providing a reasonable catch in a given year and that the catch remains sufficiently high on average over a number of years. We call this value the normalised longterm average catch (shortened to the term ‘normalised catch’). An important consideration is the threshold for the normalised catch, such that locations would generally only be considered for inclusion in a fishing ground if their values were greater than the threshold. A method for choosing a threshold is given. The boundary for a fishing ground should predominantly include only locations for which the normalised catch is greater than the threshold. Some simple criteria for designating fishing grounds are presented. The type of analytical tool needed to convert the data to a longitudelatitude grid of normalised catches and for determining boundaries on the grid according to the criteria is also discussed. The components of this process are developed using the commercial krill catch data available in the CCAMLR database.