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: Original Arten/Wirtliste der Helminthen Westösterreichs

Zusammenfassung: Ócsa bog is one of the last remnants of an ancient moorland situated between the rivers Danube and Tisza in Hungary. During the years between 1984 and 1986, the food composition of five species of fish living in the ponds and channels of a former peat mine were examined: pike (Esox lucius), mud minnow (Umbra krameri), crucian carp (Carassisu carassius), catfish (Ictalurus nebulosus) and pumpkinseed (Lepomis gibbosus). Most of the data were collected during the summer months. Overlapping food ranges of the different species were described with the Renkonen index. Much overlap was found between species Carassius carassius, Ictalurus nebulosus and Lepomis gibbosus.Das Ócsa-Feuchtgebiet liegt ca. 30 km südwestlich von Budapest, östlich des ungarischen Teils des Donautals. Ócsa ist ein Naturschutzgebiet und enthält einen der letzten und wichtigsten Reste des Marschlandes, das sich einst zwischen den Flüssen Donau und Tisza erstreckte. Die Erforschung der Vegetation dieses Gebietes begann im 19. Jahrhundert (Kerner 1867-1879)Im 20. Jahrhundert wurde das Marschland intensiv erforscht (Boros 1936, Zólyomi 1951, Komlódi 1957, Siklósi 1984). Noch heute wird das Gebiet sehr genau botanisch untersucht; nur wenige Wissenschaftler haben jedoch die Fischfauna bearbeitet. Botta et al. (1980, 1984) veröffentlichten über die Gewässer dieser Region, gaben jedoch ihr Untersuchungsgebiet nicht genau an. Andrikovics & Peer (1990) analysierten die Nahrungszusammensetzung einiger Fischarten im Ócsa-Gebiet, vor allem die von Lepomis gibbosus und Ictalurus sp.

Zusammenfassung: Multispecies virtual population analysis is an attempt to take species interactions into account when assessing the status of fish stocks. It was introduced primarily with the aim of lowering the uncertainty in the natural mortality rate as used in single species VPA and to take account of variability between years and ages by calculating inside the model the part of the mortality rate caused by predation. The output of the MSVPA is therefore – in addition to stock sizes and fishing mortality rates as in single species VPA and intraspecific – and possible species replacement are not considered and neither is predation on larvae and prerecruits. The potential relevance of multispecies models lies in long-term forecasts since short-term forecasts from multi- and single species models differ only slightly. In making long-term forecasts, predicting recruitment is of fundamental importance and the value of MSVPA in throwing light on the processes governing recruitment is limited. MSVPA is concerned primarily with calculating postrecruitment mortalities, and although in principle there is nothing to prevent prerecruitment predation mortalities being calculated, such estimates are not likely to be reliable or useful. Predation on the larval stages can be of major importance in determining recruitment but is not addressed in the MSVPA. Thus, it is quite possible that processes which are not dealt with in the MSVPA are much more important in determining the future of the fisheries. In view of all this uncertainty, it is inevitable that it will be questioned whether multispecies management advice is superior to single species advice.

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.