Zusammenfassung: Many fisheries data are commonly summarized by two statistics: mean and variance (or standard deviation). Because observed values are subject to various errors, which often are large and heterogeneous in fisheries studies, outliers commonly exist in the data. The existence of outliers biases estimation of the mean and variance if traditional estimation methods are used. Instead of assuming that errors in fisheries data follow a normal distribution with a constant variance, we propose that errors associated with observations for a variable may encompass a mixture of different levels of normally distributed errors. Based on concepts from a robust regression method, least median of squares, that is not sensitive to atypical observations in data, we develop a simple algorithm to estimate mean and standard deviation. We compare the proposed robust estimation approach with traditional methods and Tukey's biweight robust approach using simulated and field data. Based on simulations, we found little difference in estimated means and variances between the proposed and traditional methods when there were no outliers defined in simulated data. However, when outliers were defined in simulated data, the errors in estimation of the mean and its standard deviation were much smaller with the proposed method than were those estimated with traditional methods. Means and standard deviations estimated with the proposed method changed little, regardless of whether or not the simulated data were contaminated by atypical values. The proposed approach tended to have smaller estimation errors than did the robust biweight method. We demonstrate how the significance and interpretation of fisheries and ecological relationships may be adversely affected when outliers are present. We suggest using our proposed robust method to identify and down-weight outliers in estimating a mean and its standard deviation. One should justify deletion of the identified outliers using the knowledge about fish biology and environmental conditions independent of the variable assessed.

Zusammenfassung: This set of computer programs for stock assessment uses MS FORTRAN 77 version 4.1* for all the programs. The programs will compile under version 3.2 of MS FORTRAN 77, however the Open statements with file types “Unknown” must be changed to “New” or “Old” depending on the expected status of the file. The “shell” called TUNE is used to build the appropriate batch file for submission of the multiple runs used for calibration. Each of these programs is an interactive stand alone module, each can be used independently of the others. This report gives an outline of how and where these programs fit into an analytical process. A set of disks with the source code and the executable (*.EXE) files is available from the editor or the programs can be copied from the ICCAT Secretariat in Madrid; an Annex to this report containing a listing of the source code is also available. A glossary of terms commonly used in the assessment process is presented in Appendix I.