Zusammenfassung: This paper describes a method and a FORTRAN program for the estimation of von Bertalanffy growth parameters, total mortality rates and gear selection/recruitment parameters from a time series of length-frequency samples weighted by catch per effort (e.g., numbers caught per hour by length class each quarter of the year). The length frequencies are separated into normally distributed components each of which represents a cohort. This method is constructed to allow for continuous, seasonally variable recruitment all year-round and is intended to be used especially for tropical and other fish stocks displaying an appropriate recruitment pattern.

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.