Laboratory growth and food consumption data for two size classes of age 2 year yellow perch Perca flavescens, each fed on two distinct feeding schedules at 21° C, were used to evaluate the abilities of the Wisconsin (WI) and Karas–Thoresson (KT) bioenergetics models to predict fish growth and cumulative consumption. Neither model exhibited consistently better performance for predicting fish body masses across all four fish size and feeding regime combinations. Results indicated deficiencies in estimates of resting routine metabolism by both models. Both the WI and KT models exhibited errors for predicting growth rates, which were strongly correlated with food consumption rate. Consumption‐dependent prediction errors may be common in bioenergetics models and are probably the result of deficiencies in parameter values or assumptions within the models for calculating energy costs of specific dynamic action, feeding activity metabolism or egestion and excretion. Inter‐model differences in growth and consumption predictions were primarily the result of differences in egestion and excretion costs calculated by the two models. The results highlighted the potential importance of parameters describing egestion and excretion costs to the accuracy of bioenergetics model predictions, even though bioenergetics models are generally regarded as being insensitive to these parameters. The findings strongly emphasize the utility and necessity of performing laboratory evaluations of all bioenergetics models for assurance of model accuracy and for facilitation of model refinement.

Recent evidence indicates that important systematic error exists in many fish bioenergetics models (BEMs). An approach for identifying and correcting this error is demonstrated with a white crappie (Pomoxis annularis) BEM. Model-predicted trajectories of growth and cumulative consumption for 39 individual white crappie obtained from six 60-day laboratory experiments diverged from observed values by up to 42.5% and 227%, respectively, indicating systematic error in the BEM. To evaluate correlates of the systematic error, model prediction errors were regressed against three major input/output variables of BEMs that were covered by the laboratory experiments: fish body weight (80-341 g), temperature (23-30°C), and consumption level (0.5%-6.2% daily). Consumption level explained >80% of the prediction error for growth and consumption. Two multiple regression equations containing body weight, temperature, and consumption variables were developed to estimate growth prediction error (R 2 = 0.96) and consumption prediction error (R 2 = 0.86), and incorporated into the white crappie BEM to correct its predictions. Cross-validation indicated that growth and consumption prediction error was reduced 2-to 4-fold by correction. Given recent evidence of widespread systematic error and increasing application rates of BEMs, the efficient error-identification and -correction approach described appears broadly applicable and timely.Résumé : Des études récentes indiquent qu'il existe une erreur systématique importante dans plusieurs modèles bioé-nergétiques (BEM)de poissons. Nous faisons la démonstration d'une méthode pour identifier et corriger cette erreur à l'aide d'un BEM de la marigabe blanche (Pomoxis annularis). Les trajectoires de croissance et de consommation cumulative de 39 individus obtenues dans six expériences de 60 jours diffèrent des valeurs observées par autant que 42,5 % et 227 %, respectivement; il y a donc une erreur systématique dans le BEM. Afin d'évaluer les facteurs qui sont en corrélation avec l'erreur systématique, nous avons fait des régressions entre les erreurs de prédiction du modèle et trois variables d'entrée ou de sortie des BEM obtenues dans les expériences de laboratoire, soit la masse corporelle des poissons (80-341 g), la température (23-30°C) et le niveau de consommation (0,5-6,2 % par jour). Le niveau de consommation explique >80 % de l'erreur de prédiction de la croissance et de la consommation. Nous avons mis au point deux équations de régression multiple qui incluent comme variables la masse corporelle, la température et la consommation, afin d'estimer l'erreur de prédiction de la croissance (R 2 = 0,96) et de la consommation (R 2 = 0,86); nous les avons incorporées au BEM de la marigane blanche pour corriger les prédictions. Une validation croisée indique que la correction réduit par un facteur de 2-4 les erreurs dans les prédictions de croissance et de consommation. Étant donné la démonstration récente d'une erreur systématique générale dans les BEM et compte tenu de l'utilisati...

A previously developed bioenergetics model for smallmouth bass Micropterus dolomieu (Hewett and Johnson 1992) was originally constructed with data exclusively from age-0 fish but has since been used to predict growth and food consumption for both age-0 and older fish, including adults. We developed a bioenergetics model for subadult and adult smallmouth bass and compared the abilities of the two models to predict growth and food consumption for fish weighing from 100 to 270 g. Model-independent laboratory growth and food consumption data for individual subadult and adult fish were used to evaluate the performance of both models. Experimental fish were subjected to three consecutive 3-week feeding regimes: an ad libitum ration at 22ЊC, a ration of 2.5% wet body weight/d at 22ЊC, and a ration of 2% wet body weight/d at 27ЊC. Overall, the bioenergetics model developed for subadult/adult smallmouth bass produced significantly greater accuracy in estimates of relative growth rate and cumulative consumption for subadult and adult fish than did the age-0 model. The subadultϪadult model tracked observed changes in mean fish weight more closely than did the model developed for age-0 fish, and the mean percentage errors in predicting cumulative consumption were consistently lower for the subadultϪadult model. Our findings are consistent with previous studies, which have also found that bioenergetics models developed for a particular life stage of a species can yield substantially inaccurate predictions of growth and consumption for other life stages of that species. The model developed for subadult and adult fish produced better agreement with observed growth and food consumption by subadult and adult smallmouth bass than the model developed for age-0 fish and is recommended for applications involving fish weighing more than 50 g.

Population densities of smallmouth bass Micropterus dolomieu (SMB) have declined in streams of the Missouri Ozark border region since the 1940s while replacement by largemouth bass M. salmoides (LMB) has occurred in some cases. A recent field study found that two habitat variables, known to have been influenced by human activities, largely explained present densities of SMB and LMB throughout streams in this region. Densities of SMB declined with increasing maximum summer temperature (range, 23–33°C) and percent pool area while LMB densities increased with these variables. To explore these correlations from a bioenergetics perspective, we determined maximum consumption rates of SMB and LMB at 18, 22, 26, and 30°C. Consistent with the field study's findings about temperature, maximum consumption results indicated that SMB scope for growth becomes progressively restricted at temperatures higher than 22°C, whereas this does not occur until 26°C for LMB. Maximum consumption rates of SMB also averaged twice those of the LMB, indicating a much greater per capita demand for prey biomass by SMB and suggesting SMB have a lesser capacity to tolerate prey base decline than LMB. The higher consumptive demand by the SMB may relate to the field study's finding that SMB density declined with increasing pool area. Increases in pool quantity are accompanied by reduced food production and the loss of prey types known to be of importance to SMB. Findings indicate that changes in growth conditions for SMB and LMB may be a proximate cause for shifts in distribution of black bass Micropterus spp. in Ozark border region streams.

Understanding catchability—the fraction of a stock caught by a defined unit of effort—is crucial to using fisheries assessment data to index abundance. We conducted mark–recapture experiments to estimate catchability and evaluate standard boat electrofishing methods for assessing populations of Largemouth Bass Micropterus salmoides. We then used a resampling analysis to test for differences in bass CPUE (fish/h and fish/km) between two high‐density reservoirs and one low‐density reservoir and among surveys within each reservoir. We compared scenarios using surveys conducted only during (1) the standard time period (mid‐April to mid‐May) and (2) the entire assessment period (early April to mid‐June). We considered the percentage of significant differences in CPUE between the high‐density and low‐density reservoirs to represent statistical power (i.e., the ability to detect a difference in CPUE when a difference actually exists) and the percentage of significant differences in CPUE between surveys in the same reservoir to represent the false‐positive rate (i.e., the detection of a difference in CPUE when no difference in density exists). Catchability and CPUE were greatest and least variable during recapture events conducted during the standard period. The mean catchability of sub–stock length Largemouth Bass (150–200 mm) and memorable‐length bass (≥510 mm) was significantly less than those for other length categories. Statistical power exceeded 80%, and the false‐positive rate was generally less than 10% for sampling during the standard period at as few as six electrofishing sites. When including samples from outside the standard period, power was lower and the false‐positive rate was as high as 60%. Power and false‐positive rate were similar whether effort was measured in time or distance. Our results emphasize that standardized springtime boat electrofishing assessments validly index Largemouth Bass density and size structure. Received October 7, 2016; accepted February 2, 2017 Published online April 24, 2017