Zusammenfassung: A method for the multivariate analysis of fish growth in aquaculture is presented. It is derived from a linearized version of the von Bertalanffy growth function (VBGF), which, in its original form, is a bivariate regression termed the Gulland-and-Holt plot. Here, a version in form of a multiple regression equation is presented. The “extended Gulland-and-Holt plot” permits to identify and quantify the key variables controlling fish growth and permits the inclusion of these environmental and treatment variables to explain variance in growth of fish. Von Bertalanffy growth parameters K and L sub( arrow left ) are obtained, which contain the combined environmental effects on fish growth and reflect the range of culture conditions. By computing the index of growth performance ( Phi '), the obtained regression models can be used for growth prediction and decisionmaking in fish farm management and production under a wide range of environmental and management conditions. Recommendations for the design of experiments, preparation of data for analysis and actual analysis procedures are given.

Zusammenfassung: Sea surface height may be measured by a satellite-borne altimeter and its along-track slope used to infer geostrophic currents. A major difficulty is that, in general, the local geoid and satellite orbit are not known to the accuracy desired. Thus, comparison is often made between repeat flights of an altimeter along fixed ground tracks in order to infer the changes in the currents. In practice, it is convenient to calculate a mean height profile from many repeat passes and use this as a reference, so that individual altimetric profiles yield variations about this mean. It is thus important to derive a high-quality reliable estimate of the altimetric mean in order to minimize the errors in the inferred flows for the individual repeats. This work examines various methods for deriving the mean profile.

Zusammenfassung: The paper discusses the links between fish stock assessment and economics of fisheries, as perceived by a fisheries biologist. It focuses on the type and format of data required from the biological side as well as the economic side, to merge the biological and the economic methodologies into a bio-economic methodology. The biological methodology in question derives from the analytical approach, which operates with cohorts and size distributions of several stocks exploited by several fleets fishing on several fishing grounds. The economic methodology comprises costs and earnings analysis of the harvesting and the processing sectors, and computations of indicators for the economic performance of the sub-sectors. The paper is based on applications of bio-economics to tropical fisheries (targeting at shrimps). The methodology has been applied during a series of workshops and seminars organized by the Fisheries Department of FA0. The paper addresses both biologists and economists.

Zusammenfassung: Increasing numbers of students now study natural resource and environmental economics at undergraduate level, and this interest in the subject has been matched by a corresponding expansion in the number of textbooks and other learning material available for use. Unfortunately one very important aspect of natural resource economics which is often not adequately dealt with in the undergraduate literature concerns the question of dynamics: how rapidly will a resource become exploited, and what time path will it follow? These issues are particularly relevant to the management of renewable resources such as fish stocks whose survival depends on factors such as the rate of entry and exit by the exploiting firms, and the ability of the resource to recover from the effects of intense harvesting. The fault of most textbooks stems not from a failure to cover the question of dynamics, but rather that the treatment is often too advanced or mathematical for the majority of students. Computers can make a useful contribution to our understanding of the dynamics of natural resource exploitation, and in this example the case of marine commercial fishery exploited under 'open-access' conditions is considered. The quantitative assessment of environmental contaminants is a complex process. It involves nonlinear models and the characterization of variables, factors, and parameters that are distributed and dependent each other. Assessments based on point estimates are easy to perform, but since they are unreliable, Monte Carlo simulations have become a standard procedure. Simulations pose two challenges: They require the numerical characterization of parameter distributions and they do not account for dependencies between parameters. This paper offers strategies for dealing with both challenges. The first part discusses the characterization of data with the S-distribution. This distribution offers several advantages, which include simplicity of numerical analysis, flexibility in shape, and easy computation of quantiles. The second part outlines how the S-distribution can be used for hierarchical Monte Carlo simulations. In these simulations the selection of parameter values occurs sequentially, and each choice depends on the parameter values selected before. The method is illustrated with preliminary simulation analyses that are concerned with mercury contamination in king mackerel (Scomberomorus cavalla). It is demonstrated that the results of such hierarchical simulations are generally different from those of traditional Monte Carlo simulations.