Fish activity costs are often estimated by transforming their swimming speed in energy expenditures with respirometry models developed while forcing fish to swim against a flow of constant velocity. Forced swimming models obtained using a procedure that minimizes flow heterogeneity may not represent the costs of swimming in rivers characterized by turbulence and by a wide range of instantaneous flow velocities. We assessed the swimming cost of juvenile Atlantic salmon (Salmo salar) in turbulent flows using two means (18 and 23 cm·s–1) and two standard deviations of flow velocity (5 and 8 cm·s–1). Twenty respirometry experiments were conducted at 15 °C with fish averaging 10 g. Our results confirmed that swimming costs are affected by the level of turbulence. For a given mean flow velocity, swimming costs increased 1.3- to 1.6-fold as turbulence increased. Forced swimming models under estimated actual swimming costs in turbulent flow by 1.9- to 4.2-fold. Spontaneous swimming models overestimated the real cost of swimming in turbulent flow by 2.8- to 6.6-fold. Our analyses suggest that models in which both the mean and the standard deviation of flow velocity are explicitly represented are needed to adequately estimate the costs of swimming against turbulent flows.

We used the Kitchell et al. (J. Fish. Res. Board Can. 34: 1922–1935) bioenergetics model and field derived estimates of growth and consumption rates to estimate the quantity of energy allocated to activity by 28 combinations of yellow perch (Perca flavescens) age class and population. Activity costs among populations ranged from 0 to 40% of the perch bioenergetics budget. We further evaluated the influence of activity rates on the food consumption estimates predicted by the Kitchell et al. model and the model proposed by Kerr (Can. J. Fish. Aquat. Sci. 39: 371–379). As suggested by Kerr, activity costs increased as food consumption increased. However, we found no significant relationship between predicted and observed food consumption estimates for either model. The magnitude of, and the among-population variance in, the quantity of energy allocated to activity is consistent with our hypothesis that this component of the bioenergetics budget of fishes has the potential to contribute meaningfully to the explanation of inter-population differences in perch growth and, by extension, to the variance in growth of other actively foraging fish species.

Papers and panel discussions given during a 1992 symposium on bioenergetics models are summarized. Bioenergetics models have been applied to a variety of research and management questions relating to fish stocks, populations, food webs, and ecosystems. Applications include estimates of the intensity and dynamics of predator–prey interactions, nutrient cycling within aquatic food webs of varying trophic structure, and food requirements of single animals, whole populations, and communities of fishes. As tools in food web and ecosystem applications, bioenergetics models have been used to compare forage consumption by salmonid predators across the Laurentian Great Lakes for single populations and whole communities, and to estimate the growth potential of pelagic predators in Chesapeake Bay and Lake Ontario. Some critics say that bioenergetics models lack sufficient detail to produce reliable results in such field applications, whereas others say that the models are too complex to be useful tools for fishery managers. Nevertheless, bioenergetics models have achieved notable predictive successes. Improved estimates are needed for model parameters such as metabolic costs of activity, and more complete studies are needed of the bioenergetics of larval and juvenile fishes. Future research on bioenergetics should include laboratory and field measurements of key model parameters such as weight‐dependent maximum consumption, respiration and activity, and thermal habitats actually occupied by fish. Future applications of bioenergetics models to fish populations also depend on accurate estimates of population sizes and survival rates.

We evaluated the ability of numerical habitat models (NHM) to predict the distribution of juveniles of Atlantic salmon (Salmo salar) in a river. NHMs comprise a hydrodynamic model (to predict water depth and current speed for any given flow) and a biological model (to predict habitat quality for fish using water depth, current speed, and substrate composition). We implemented NHMs with a biological model based on (i) preference curves defined by the ratio of the use to the availability of physical conditions and (ii) a multivariate logistic regression that distinguished between the physical conditions used and avoided by fish. Preference curves provided a habitat suitability index (HSI) ranging from 0 to 1, and the logistic regression produced a habitat probabilistic index (HPI) representing the probability of observing a parr under given physical conditions. Pearson's correlation coefficients between HSI and local densities of parr ranged from 0.39 to 0.63 depending on flow. Corresponding values for HPI ranged from 0.81 to 0.98. We concluded that HPI may be a more powerful biological model than HSI for predicting local variations in fish density, forecasting fish distribution patterns, and performing summer habitat modelling for Atlantic salmon juveniles.

We compared estimates of daily ration developed using the theoretically rigorous and logistically demanding Elliott and Persson model and the more easily applied Eggers model which is infrequently used because of its assumptions about rigid fish feeding periodicity. Comparisons were based on ten 24-h samplings of six different yellow perch (Perca flavescens) populations. Daily ration estimates from the two models did not differ significantly. This consistency occurred in spite of the fact that in some cases the observed feeding periodicity violated the assumptions of the Eggers model. A simulation model demonstrated that 95% confidence intervals were smallest for the Eggers estimates and that the Eggers model was more robust than the Elliott and Persson model to changes in both sampling frequency and number offish sacrificed at each sampling event. The latter proved particularly sensitive to changes in sampling frequency. We concluded that the two models provide estimates of daily ration comparable in magnitude and accuracy and consequently that the restriction of the Eggers model to fish with rigid feeding periodicity is not justified. Furthermore, the Eggers model, because of its robustness, reduces the sampling requirements to determine daily ration, and hence, permits its estimation on a more frequent basis.