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: 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.