Factors affecting rainbow trout growth and condition in eastern Washington lakes

Background/Question/Methods

Rainbow trout growth and condition were examined in 17 ecologically diverse lakes from which physical, limnological, and biological parameters were sampled. Parameters were correlated with rainbow trout growth and condition to identify factor(s) that predict rainbow trout growth and condition across eastern Washington lakes stocked annually with rainbow trout fry.  We tested the hypothesis Ho: the environmental variables examined do not predict rainbow trout growth and condition.  Data were natural log transformed for better fit, and were analyzed by season.  Several models were identified using stepwise multiple regression analysis and general linear modeling which significantly predicted trout growth or condition using one or more of the biotic and abiotic independent variables.

Results/Conclusions

Trends in trout growth and condition correlated to biomass of odonates, amphipods, caddisflies, calanoid copepods and density of dipterans and hemipterans were generally positive regardless of season.  Trout growth and condition were inversely proportional to mean and maximum lake depth.  The best regression models explained as much as 95% of lake-to-lake variation in trout growth and condition.  General linear models, which included presence and absence of other fishes, explained 58% to 99% of the variation in trout condition and identified several negative relationships with rainbow trout condition.  Stocking density, presence of largemouth bass, green sunfish, brown trout, and tiger trout negatively affected rainbow trout condition.  The collection of significant models suggests that rainbow trout stocked into eastern Washington lakes realize higher growth rates and better condition in the presence of abundant forage base and in the absence of competition or predation by resident fish species.  We identified several environmental variables which may be used as single predictors, or in a suite of multiple predictors, to significantly predict rainbow trout growth and condition.  Thus, the environmental variables used in this study, or perhaps additional variables, could be monitored and used by regional resource agencies in managing rainbow trout fisheries.