Prey abundance, channel structure and the allometry of growth rate potential for juvenile trout

Abstract

The application of a drift-foraging bioenergetic model to evaluate the relative influence of prey abundance (invertebrate drift) and habitat (e.g. pool frequency) on habitat quality for young-of-the-year (YOY) and yearling juvenile cutthroat trout, Oncorhynchus clarki (Richardson) is described. Experiments and modelling indicated simultaneous limitation of fish growth by prey abundance and habitat, where depth and current velocity limit the volume of water and prey flowing through a fishÕs reactive field as well as swimming costs and prey capture success. Predicted energy intake and growth increase along a depth gradient, with slower deeper pool habitat generating higher predicted growth for both YOY and yearling trout. Bioenergetic modelling indicated that fish are constrained to use progressively deeper habitats to meet increasing energy requirements as they grow. Sensitivity of growth to prey abundance identified the need to better understand how variation in invertebrate drift and terrestrial drop affects habitat quality and capacity for drift-feeding fishes.K E Y W O R D S : bioenergetic modelling, habitat limitation, habitat quality, habitat quantity, invertebrate drift.

Modeling the influence of flow on invertebrate drift across spatial scales using a 2D hydraulic model and a 1D population model

“…Bioenergetic models are typically used to assess performance of individual fish across gradients of physical habitat variables and food availability (e.g. Rosenfeld and Taylor, 2009). With additional assumptions, they have been used to predict larger spatial patterns of biomass distribution or capacity (Grossman et al, 2002;Hayes et al, 2007;Hughes, 1998), but a remaining challenge is linking spatial patterns of growth and survival to population viability (Anderson et al, 2006b;Armstrong and Nislow, 2012;Frank et al, 2011;Locke et al, 2008).…”

Causes and consequences of invertebrate drift in running waters: from individuals to populations and trophic fluxes

“…While drift-foraging models have been successful in terms of exploring the energetic trade-offs involved in foraging position choices and behavioural strategies of drift-feeding fishes (Fausch 1984;Nielsen 1992;Hughes 1998;Piccolo et al 2014), absolute predictions are very sensitive to estimated drift abundance as an input parameter (e.g., Rosenfeld and Taylor 2009). Uncertainty in how drift varies spatially and temporally complicates attempts to quantitatively predict drift concentration (see above section on Spatial and temporal drift dynamics) and consequently also reduces confidence in predictions from drift-foraging models.…”

“…Drift entry rates are usually adjusted as part of the model-fitting process; using settling rates as fixed parameters from the literature, entry rates are adjusted to generate observed drift concentrations (e.g., Hayes et al 2007;Anderson et al 2013Railsback et al 2003). Other applications have either assumed constant drift concentration (Rosenfeld and Taylor 2009) or that entry is directly proportional to benthic density (Kennedy et al 2014). The predictive power of bulk community drift models may be contingent on the relative contribution of active versus passive processes to drift dynamics.…”

Quantifying the role of woody debris in providing bioenergetically favorable habitat for juvenile salmon

“…Recently developed bioenergetic models allow the prediction of growth rates and the extent of favorable locations for driftfeeding fish in rivers (Booker et al, 2004;Hayes et al, 2007Hayes et al, , 2000Railsback et al, 2013;Rosenfeld and Taylor, 2009). Empirically derived coefficients depicting relationships between swimming speed, temperature and prey intake are used in these models to define growth rates, while drift density and capture efficiency coefficients express energy intake.…”