Process-based modelling of invertebrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids

Hayes, John W.; Hughes, Nicholas F.; Kelly, Lon H.

doi:10.1016/j.ecolmodel.2007.04.032

Cited by 114 publications

(132 citation statements)

References 58 publications

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“…In the absence of empirical data, we introduced invertebrates at 40% of the grid node depth to be consistent with the model predictions of the depth-integrated velocity, which is calculated at 40% of the flow depth. Furthermore, such a choice is at least consistent with reports of substantial drift densities in upper portions of the water column (also see Hayes et al, 2007). The concentration of organisms was computed at each node in the computational grid as the simulations progressed and the organisms were transported downstream.…”

Section: Overview Of Approachmentioning

confidence: 68%

“…Drift transport was modeled using the flow models calibrated at discharges Q = 6.4 m 3 /s (baseflow conditions) and Q = 32.5 m 3 /s (∼75% of the bankfull flow). The effect of settling velocity (ω s ) and dispersion (D H and D V ) have been shown to play important roles on the pattern of drift transport in previous models (Ciborowski, 1983;Hayes et al, 2007). An added complication arises from the fact that many stream macroinvertebrates exhibit control over their behavior in the drift and settle at rates that differ from similar sized inanimate particles (Allan and Feifarek, 1989;Campbell, 1985;Elliott, 1971a;Oldmeadow et al, 2010;Otto and Sjostrom, 1986).…”

Section: Overview Of Approachmentioning

confidence: 99%

“…Recent notable attempts have been made to integrate flow variability, variability in food delivery rates and bioenergetics to model discharge-dependent fish biomass (e.g. Hayes et al, 2007). However, the combination of context dependence and computational expense hinders the ability of current approaches to scale-up results to whole reaches (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…A key biotic variable in fish bioenergetics models is food delivery rate (Chipps and Wahl, 2008;Ney, 1993;Pecquerie et al, 2011) which many studies assume to be either uniform or simple functions of velocity (e.g. see references in Hayes et al, 2007). Frequently missing are the known effects of physical habitat and flow on the dispersal and population dynamics of benthic macroinvertebrates (fish food) and feedbacks between fish and their prey.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Anderson

Harrison

Nisbet

et al. 2013

Ecological Modelling

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a b s t r a c tMethods for creating explicit links in environmental flow assessments between changes in physical habitat and the availability and delivery rate of macroinvertebrates that comprise fish diets are generally lacking. Here, we present a hybrid modelling approach to simulate the spatial dynamics of macroinvertebrates in a section of the Merced River in central California, re-engineered to improve the viability of Chinook salmon. Our efforts focused on quantifying the influence of the hydrodynamic environment on invertebrate drift dispersal, which is a key input to salmon bioenergetics models. We developed a twodimensional hydrodynamic model that represented flow dynamics well at baseflow and 75% bankfull discharges. Hydraulic predictions from the 2D model were coupled with a particle tracking algorithm to compute drift dispersal, where the settling rates of simulated macroinvertebrates were parameterized from the literature. Using the cross-sectional averaged velocities from the 2D model, we then developed a simpler 1D representation of how dispersal distributions respond to flow variability. These distributions were included in 1D invertebrate population models that represent variability in drift densities over reach scales. Dispersal distributions in the 2D simulation and 1D representation responded strongly to spatial changes in flow. When included in the 1D population model, dispersal responses to flow 'scaled-up' to yield distributions of drifting macroinvertebrates that showed a strong inverse relationship with flow velocity. The strength of the inverse relationship was influenced by model parameters, including the rate at which dispersers settle to the benthos. Finally, we explore how the scale of riffle/pool variability relative to characteristic length scales calculated from the 1D population model can be used to understand drift responses for different settling rates and at different discharges. We show that, under the range of parameter values explored, changes in velocity associated with transitions between riffles and pools produce local changes in drift density of proportional magnitude. This simple result suggests a means for confronting model predictions against field data.

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Section: Overview Of Approachmentioning

confidence: 68%

Section: Overview Of Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Anderson

Harrison

Nisbet

et al. 2013

Ecological Modelling

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show abstract

“…The heterogeneous current velocities within feeding habitat patches induce a patchy distribution of drifting food supply and different opportunities to capture prey, which may explain the aggregation of individuals in feeding patch areas with good physical conditions (Baras 1997, Huber & Kirchhofer 1998. A process-based modeling approach has shown how heterogeneous current velocities affect invertebrate drift density and numbers of drift-feeding salmonids (Hayes et al 2007). This was predicted by optimal foraging theory, where feeding patch choice is viewed as a function of optimal foraging where fish distribute themselves in order to maximize energy intake in high-quality patches (Matthews 1998).…”

Section: Patterns Of Species Distributionmentioning

confidence: 99%

Using a continuous riverscape survey to examine the effects of the spatial structure of functional habitats on fish distribution

Pichon¹,

Talès²,

Gorges³

et al. 2015

Journal of Freshwater Ecology

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To fill the gap between microhabitat and landscape scale habitat models for freshwater fish, it is becoming increasingly common practice to adopt a continuous view of riverscapes, thus allowing a better understanding of the processes in place at the river management level (segments of 1À100 km). The aim of this study was to test the effects of the spatial structure of habitat on fish distribution at this scale. Inferred habitat relationships were generated using spatial metrics adapted from landscape ecology. These were calculated for two species of multi-habitat cyprinid fish in a 25-km long segment of the Seine River. A spatially continuous survey was then designed to acquire fish sampling data relating to the riverscape. Explanatory models were devised to quantify the extent to which environmental and spatial variables could describe fish distribution patterns. Spatially continuous sampling of feeding habitats at dawn and dusk provided greater understanding of the spatial distribution of common barbel (Barbus barbus, L.) and nase (Chondrostoma nasus, L.). Fish observations were aggregated longitudinally in neighboring feeding habitat patches, with the highest abundance found in patches with the best local conditions. The species were present for large feeding patches, as well as for a higher proximity index for those patches. This result emphasized the importance of the supplementation of feeding habitats. By quantifying spatial habitat relationships using spatial metrics, it was possible to identify the best suited configuration of functional habitats to the needs of shoals. At present, most conservation work focuses on restoring local habitats. There is also growing interest in large-scale fish management, which has been encouraged by the advent of metapopulation theory. This study highlights the need for greater work at a third, intermediate scale that is no less significant: restoring daily and seasonal movements between functional habitats.

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Hydraulic Modelling Approaches for Ecohydraulic Studies: 3D, 2D, 1D and Non‐Numerical Models

Tonina

Jorde²

2013

Ecohydraulics

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Process-based modelling of invertebrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids

Cited by 114 publications

References 58 publications

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

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

Using a continuous riverscape survey to examine the effects of the spatial structure of functional habitats on fish distribution

Hydraulic Modelling Approaches for Ecohydraulic Studies: 3D, 2D, 1D and Non‐Numerical Models

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