We quantified the sensitivity of predicted rates of growth and consumption to parameter variation for models of yellow perch (Perca flavescens), largemouth bass (Micropterus salmoides), and alewife (Alosa pseudoharengus). We used statistical analyses of the results of Monte Carlo simulations to rank parameter importance. The order of parameter importance was model specific, although the results emphasized the need for accurate estimation of the realized fraction of maximum consumption rate (P) and allometric parameters for consumption (a1, b1) and respiration (a2, b2). Excretion and egestion parameters contributed little to prediction errors. The Monte Carlo methods were used to examine the relative importance of parameter variation and diet composition, an external forcing function, on forecasts of alewife growth. If standard deviations of model parameters were known within 2% of their expected values, uncertainty in diet composition could contribute as much as 47% of the variance in predicted alewife weight. When standard deviations of model parameters are realistically defined, diet uncertainty contributed less than 10% of the variance in predicted weight.

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Bioenergetics modeling is a widely used tool in fisheries management and research. Although popular, currently available software (i.e., Fish Bioenergetics 3.0) has not been updated in over 20 years and is incompatible with newer operating systems (i.e., 64‐bit). Moreover, since the release of Fish Bioenergetics 3.0 in 1997, the number of published bioenergetics models has increased appreciably from 56 to 105 models representing 73 species. In this article, we provide an overview of Fish Bioenergetics 4.0 (FB4), a newly developed modeling application that consists of a graphical user interface (Shiny by RStudio) combined with a modeling package used in the R computing environment. While including the same capabilities as previous versions, Fish Bioenergetics 4.0 allows for timely updates and bug fixes and can be continuously improved based on feedback from users. In addition, users can add new or modified parameter sets for additional species and formulate and incorporate modifications such as habitat‐dependent functions (e.g., dissolved oxygen, salinity) that are not part of the default package. We hope that advances in the new modeling platform will attract a broad range of users while facilitating continued application of bioenergetics modeling to a wide spectrum of questions in fish biology, ecology, and management.

We hypothesized that visual acuity in fishes and thus reactive distance should increase with fish size; visual acuity depends on eye lens diameter and cone density in the retina, and eye lens diameter increases with fish size. Though cone density declines in larger fish, we expected this effect to be relatively small. We tested this hypothesis for a behavioral measure of visual acuity, the reactive distance of bluegill (Lepomis macrochirus) sunfish to zooplankton prey, in aquaria (375 L) for fish from 27 to 162 mm standard length. Reactive distance increased nonlinearly with fish size; the rate of increase in reactive distance slows in larger fish. For fish of a given size, reactive distance was dependent on prey size, but visual angle measured from the fish eye was nearly constant. Whereas lens diameter and visual acuity increase with fish size in bluegills, the acuity of larger fish is less than expected from eye lens diameter alone. This is probably a result of cone density decreasing with fish size, as has been found for other fishes. The observed fish-size-dependent differences in reactive distance imply very large differences in visual volumes and encounter rates with prey among size-classes of bluegills. Habitat segregation among bluegill size-classes may prevent the intense intraspecific competition for prey that would be expected, in part, from the superior visual acuity of larger fish.Key words: foraging, reactive distance, size-class interactions, bluegill, Lepomis macrochirus; predation, visual acuity, zooplankton

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