Examining feeding strategies and position choice of drift-feeding salmonids using an individual-based, mechanistic foraging model

Abstract

We demonstrated the ability of a mechanistic habitat selection model to predict habitat selection of brown trout (Salmo trutta) and mountain whitefish (Prosopium williamsoni) during summer and winter conditions in the Blacksmith Fork River, Utah. By subtracting energy costs and losses from the gross energy intake rate (GEI) obtained through simulation of prey capture, the model calculates the potential net energy intake rate (NEI) of a given stream position, which is essentially the rate of energy intake available for growth and reproduction. The prey capture model incorporates the size, swimming speed, and reaction distance of the fish; the velocity, depth, temperature, and turbidity of the water; and the density and size composition of the drifting invertebrates. The results suggest that during both summer and winter, the brown trout and mountain whitefish in our study reach avoided locations providing low NEI and preferred locations providing a high ratio of NEI to the swimming cost (SC) at the focal position of the fish (NEI/SC). This supports the idea that the drift-feeding fish in this study selected stream positions that provided adequate NEI for the least amount of swimming effort.

Spatiotemporal variation in flow-dependent recruitment of long-lived riverine fish: Model development and evaluation

“…To account for direct flow-dependence in foraging by shovelnose sturgeon (Modde and Schmulbach, 1977;Seibert et al, 2011), we use the drift foraging model by Guensch et al (2001) to calculate the realized encounter rate (Eprey,i and Eintra); drift-feeding sturgeon are assumed to encounter and capture prey within an area defined by fish swimming speed and water velocity. Eprey,i and Eintra are calculated by a randomly drawn number from a Poisson distribution with potential encounter rate (ERprey i or intra, number • s -1 ) as the number of trials, calculated as…”

“…where ΔѲ is an incremental angle perpendicular to the flow vector (MCA is divided into 10 equal segments, Guensch et al, 2001), and MCDi is the maximal capture (or competition) distance (m).…”

Defining environmental river flow requirements – a review

“…More physicallybased models of fish habitat use (e.g. Guensch et al, 2001;Booker et al, 2004) also have potential to improve environmental flow methods (Hardy, 1998).…”

Nature and Functions of Cover for Riverine Fish.

“…The authors suggested that by creating more feeding sites that are energetically favourable for trout during summer, boulders may indirectly enhance their body condition and thus overwinter survival. The energy-optimization utility of these foraging sites has been revealed by simple intake rate/growth maximization models which try to predict habitat selection by salmonids in experimental streams (FAUSCH, 1984) and in the field (HUGHES and DILL, 1990;HUGHES, 1992;GUENSCH et al, 2001). These models showed that individuals ranked positions according to their potential profit by combining hydraulic, drift and other biological parameters (detection distance of prey, presence of competitors).…”