Describing growth based on landscape characteristics and stocking strategies for rainbow trout

Abstract The achievement of target growth rates of stocked fish in a particular environment is an important component of recreational fisheries management; if stocked fish do not achieve a desired size structure, then angling effort and satisfaction may be lower than anticipated. We developed a growth model for rainbow trout ( Oncorhynchus mykiss ) based on a Bayesian hierarchical analysis of growth data from 142 gillnet assessments across the province of British Columbia. The growth equation was defined as a von Bertalanffy function with environmental and stocking covariates applied to the function’s asymptotic length ( L ∞ ) and metabolic rate ( K ) parameters. Key factors defining growth for the best performing model were the time spent in lake based on accumulated growing degree days, the life-stage at stocking, stocking density, and the stocked strain. Calculating time in-lake in terms of growing degree days experienced by fish instead of calendar days in-lake improved the prediction of growth. We explore examples of how to use this information, such as identifying stocking rates needed to achieve particular size thresholds given size-structure objectives for a stocked lake fishery. This analysis helps managers determine how to efficiently distribute hatchery-reared fish across the landscape and recognize limits to growth given particular environmental constraints while also tailoring to the diversity of angler preferences and expectations of the fishery.