Net rate of energy intake predicts reach-level steelhead (Oncorhynchus mykiss) densities in diverse basins from a large monitoring program

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Abstract

Substantial research effort has been devoted to understanding stream-dwelling salmonids’ use of summer rearing and growth habitat, with a subset of studies focusing on foraging position selection and the energetic trade-offs of differential habitat use. To date, however, cost–benefit analyses for most foraging model studies have focused on small sampling areas such as individual habitat units. To address this knowledge gap, we applied a mechanistic foraging model to 22 stream reaches (100–400 m) from two watersheds within the Columbia River Basin. We found a strong, positive correlation (R2 = 0.61, p < 0.001) between predicted carrying capacities and observed fish densities. Predicted proportion of suitable habitat was weakly correlated with observed fish density (R2 = 0.18, p = 0.051), but the mean net rate of energy intake prediction in sampling reaches was not a significant predictor of observed fish biomass. Our results suggest spatial configuration of habitat, in addition to quantity and quality, is an important determinant of habitat use. Further, carrying capacity predicted by the model shows promise as a habitat metric. We also evaluated the feasibility of applying this data-intensive modeling approach in a large-scale monitoring program to examine habitat quality and quantity. Though the approach can be computationally expensive, we feel the model’s ability to integrate physical habitat metrics (e.g., depth, velocity) with important biological considerations like food availability and temperature is a benefit that far outweighs associated costs. We feel this modeling approach has great potential as a tool to help understand habitat use in drift-feeding fishes.

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Wall, C. E., Bouwes, N., Wheaton, J. M., Saunders, W. C., & Bennett, S. N. (2016). Net rate of energy intake predicts reach-level steelhead (Oncorhynchus mykiss) densities in diverse basins from a large monitoring program. Canadian Journal of Fisheries and Aquatic Sciences, 73(7), 1081–1091. https://doi.org/10.1139/cjfas-2015-0290

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