Zusammenfassung: The Fabens method is commonly used to estimate growth parameters k and l in the von Bertalanffy model from tag-recapture data. However, the Fabens method of estimation has an inherent bias when individual growht is variable. This paper present an asymptotocally unbiasses method using the maximum-likelihood approach that takes account of individual varibility in both maimum lenth and age-at-tagging. ...

Zusammenfassung: The authors sampled fishes and measured microhabitat in series of contiguous habitat units (riffles, runs, pools) in three Virginia streams. Monte Carlo simulations were used to construct hypothetical series of habitat units, then examined how number of species, similarity in relative abundances, and number of microhabitats accumulated with increasing number of habitat units (i.e., sampling effort). The curves indicated that 90% of the species present were usually found by sampling 5 to 14 habitat units (stream length of 22-67 stream widths). Estimates of species relative abundances required less sampling effort for a given accuracy than estimates of number of species. Rates of species accumulation (with effort) varied among streams and reflected discontinuity in species distributions among habitat units. Most discontinuity seemed to be due to low population density rather than to habitat selectivity. Results from an Illinois stream corroborated our findings from Virginia, and suggested that greater sampling effort is needed to characterize fish community structure in more homogeneous stream reaches.

Zusammenfassung: Several new models are developed to estimate the velocity and diffusion of a population from tagging data. The new estimators apply the inverse principle to the individual trajectories of recovered tags rather than to their local abundance. These models require fewer assumptions and less information than do published abundance-based methods. Techniques are presented for a variety of circumstances, and both discrete and continuous parameterizations of the velocity field are included. The sensitivity of the estimators to violations of the assumptions was examined numerically by using stochastic simulations. The results suggest that the estimators are fairly robust but may fail under certain conditions. Extensions to accommodate these situations are discussed.

Zusammenfassung: Often only simple relative abundance time series and basic growth and (or) survival estimates are available for assessing impacts of fishing and environmental factors. Assessment then involves fitting production models to the series, while forcing the model with observed catch or effort series. A key uncertainty in this approach is how to deal with recruitment variations due to factors other than stock size. A dynamic programming algorithm can be used to compute maximum likelihood estimates of the recruitment anomaly sequence, given prior knowledge of growth parameters, the natural survival rate, and proportion of the variation in the relative abundance index that is due to abundance measurement errors.

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.