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: The statistical model used is extended to estimate abundance from commercial catch-at-age data for many of the major commercial fish species in the world. The model combines commercial catch-at-age data and research survey estimates of fish abundance; extends the model to allow correlated errors among ages within a year for the survey estimates of fish abundance. Method is formulated for modeling the fishing mortality on the oldest ages of the fish caught. Estimates are obtained using maximum likelihood. The level of correlation among ages is sufficiently large to produce large biases in the standard methods for some stocks. The statistical model that includes correlated errors greatly reduces bias and increases efficiency if the correlation in the estimation error is large.

Zusammenfassung: Estimation of von Bertalanffy growth parameters has received considerable attention in fisheries research. Since Sainsbury much of this research effort has centered on accounting for individual variability in the growth parameters. In this paper we demonstrate that, in analysis of tagging data, Sainsbury's method and its derivatives do not, in general, satisfactorily account for individual variability in growth, leading to inconsistent parameter estimates (the bias does not tend to zero as sample size increases to infinity). The bias arises because these methods do not use appropriate conditional expectations as a basis for estimation.