channel structure and growth rate

8/4/2019 Channel Structure and Growth Rate

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Prey abundance, channel structure and the

allometry of growth rate potential for juvenile trout

J . S . R O S E N F E L D

B.C. Ministry of the Environment, Vancouver, BC, Canada

J . T A Y L O R

Jacques Whitford Environmental, Burnaby, BC, Canada

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 fish s 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.

Introduction

Physical habitat structure and food availability (prey

abundance) are key factors affecting habitat use and

production of stream fishes. Habitat structure influ-

ences metabolic (e.g. swimming) costs, the ability of

fish to encounter and capture prey items and vulner-

ability to predation; prey abundance (encounter rate)

directly influences energy intake that a fish experiences

in any given habitat (Hill & Grossman 1993; Gross-

man, Rincon, Farr & Ratajczak 2002; Hughes, Hayes,

Shearer & Young 2003). Habitat structure and prey

abundance therefore jointly determine both the quan-

tity of habitat that generates positive growth and

survival for an organism across a landscape (i.e. the

area of useable habitat) as well as the quality of that

habitat (realised growth and survival rates in different

habitat types). The influence of physical habitat

structure on habitat use by stream fishes has received

enormous attention and been the focus of innumerable

studies. By contrast, few studies have considered the

role of prey abundance in habitat selection (e.g.

Hughes 1992; Nislow, Folt & Parrish 2000; Guensch,

Hardy & Addley 2001), and fewer still how habitat and

food interact to determine the distribution of useable

habitats in streams (e.g. Poff & Huryn 1998; Thomp-

son, Petty & Grossman 2001). Consequently, there is a

need to develop both theory and modelling tools to

predict the outcome of environmental impacts that

alter habitat structure and prey abundance in streams.

Fish production is the outcome of complex interac-

tions between habitat and prey abundance (Grant,

Steingrimsson, Keeley & Cunjak 1998; Poff & Huryn

1998), and separating their independent effects on

habitat quality is difficult. Stream-rearing salmonids

are exceptional in the simplicity of their foraging mode

feeding on drifting invertebrates while swimming at a

fixed focal point in the water column. Consequently,

bioenergetic and foraging models developed for salmo-

nids can be used to convert the effects of habitat

and prey abundance to estimates of energy intake,

expenditure and, ultimately, growth. This allows direct

Correspondence: Jordan Rosenfeld, B.C. Ministry of the Environment, 2202 Main Mall, Vancouver, BC, Canada V6T 1Z4

(e-mail: [email protected])

Fisheries Management and Ecology, 2009, 16, 202218

doi: 10.1111/j.1365-2400.2009.00656.x 2009 Crown in the right of Canada.

Fisheries Managementand Ecology


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quantification of the independent effects of prey abun-

dance (invertebrate drift) and physical habitat (water

depth, velocity, channel cross-section configuration) on

growth rate potential (where growth rate potential

(Brandt, Mason & Patrick 1992) represents density-independent growth, i.e. the maximum growth rate

realised in the absence of competition and predation).

However, the suitability of drift-feeding salmonids as a

model system to understand how habitat and food

jointly control habitat quality depends on how well

bioenergetic foraging models predict actual growth

(Ney 1993). Fortunately, the ability to measure the

growth of individuals experimentally confined to dif-

ferent habitats in natural streams (e.g. Rosenfeld &

Boss 2001; Harvey, White & Nakomoto 2005) allows

testing of bioenergetic models for drift feeding fish in

ways that are extremely difficult for many other taxa(e.g. pelagic fishes or birds).

The size of the organism further modifies the

influence of food and habitat on individual growth

through the complex allometry of metabolism and

habitat relationships (Peters 1983). Most of these

relationships scale non-linearly with organism size,

making it difficult to anticipate precisely how habitat

quality will change with fish size and prey abundance.

However, because the absolute energy requirements of

fish (i.e. prey intake required for basal metabolism and

growth) increase with size, the subset of habitats that

can deliver adequate drift to support growth of larger

fish should be limited in small streams and can be

expected to decrease as fish grow larger (Rincon &

Lobon-Cervia 2002). Similarly, the allometry of growth

should result in a proportionally greater growth

response of smaller fish to prey enrichment.

This study describes and calibrates a bioenergetic

model to predict the growth rate of drift-feeding fish

[juvenile cutthroat trout, Oncorhynchus clarki (Rich-

ardson) and coho salmon, Oncorhynchus kisutch (Wal-

baum)] by comparing observed and modelled growth

of individual fish from experiments in a natural stream

and artificial channels. Modelling scenarios were then

used to assess the effect of independently varying bothprey abundance and habitat structure on growth rate

potential for juvenile trout in a small stream. Specific

objectives were: (1) to determine how quality differs

between habitats that represent the fundamental con-

stituents of stream channels (e.g. pools vs riffles); (2) to

assess how the extent and quality of useable habitat

changes with fish size and whether there are size-

related thresholds in the ability of fish to exploit

habitats; and (3) to determine how physical habitat

and prey abundance jointly limit habitat quality in

small streams by modelling the independent effects of

increasing pool frequency vs increasing prey abun-

dance on growth rate potential for juvenile trout.

Predictions were: (1) that the allometry of increasing

energy requirements as fish grow should reduce both

the extent and the quality of habitat available to largerdrift-feeding fish in small streams (i.e. small trout

should be capable of exploiting habitats with lower

energy intake than larger trout); (2) that both physical

habitat and prey abundance simultaneously limit

habitat quality (growth rate potential) in streams;

and (3) that increasing prey abundance should permit

exploitation of habitats that are bioenergetically

unavailable at lower productivities.

Methods

The structure of the drift-foraging and bioenergeticmodel is presented below, followed by a description of

the experimental growth data used to calibrate it and

the modelling scenarios used to assess food and habitat

effects on fish growth.

Overview of the bioenergetic model

The bioenergetic model used was a modification of an

earlier model for drift-feeding fish (Hughes & Dill

1990; Hughes et al. 2003) that calculates energy intake

based on the volume of water flowing past the focal

point of a fish within its reactive distance to three size-

classes of invertebrate prey. Energy expenditures were

based on the swimming costs experienced by an

individual fish at its focal point and the additional

costs of leaving the focal point to intercept prey. The

net energy left over for growth is simply energy intake

less energy losses and expenditures. The model differs

from many earlier models by including: (1) a relation-

ship that adjusts prey capture success for water

velocity, fish size and distance of prey from the focal

point of the fish (but see Van Winkle, Jager, Railsback,

Holcomb, Studley & Baldrige 1998; Nislow, Folt &

Parrish 1999 and Railsback, Stauffer & Harvey 2003

for similar implementations); (2) a scalar that adjustsenergy expenditures for increased swimming costs

associated with turbulence at higher velocities; and

(3) a correction factor to adjust bioenergetic growth

estimates for any positive bias associated with

increased prey consumption (Bajer, Whitledge, Hay-

ward & Zweifel 2003). These additions improve the

accuracy of model predictions, as described in more

detail below.

Inputs to the bioenergetic model (see Hughes & Dill

1990; Rosenfeld & Boss 2001) are invertebrate drift

concentration (mg m)3 dry weight) for three size

C H A N N EL S T R U C T U R E A N D A L L OM E T R Y O F T R O U T G R O W T H 20 3

2009 Crown in the right of Canada.


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classes of invertebrate prey (5.0 mm), fish mass (g wet weight, for metabolic

calculations), water velocity (cm s)1) and depth (cm) at

the focal point of the fish (to model swimming costs),

and depth and velocity at 60% of total depth at 20-cmintervals along a transect through the focal point of the

fish (perpendicular to flow) and along an additional

transect 20 cm upstream of the focal point (to model

energy intake).

Velocity and depth transects were used to estimate

discharge flowing past the focal point of a fish [water

volume per unit time, calculated as the product of

cross-sectional area (CA) and velocity (V)] within a

fishs reactive distance to three size-classes of drifting

prey (Hughes & Dill 1990; see Table 1). Three size-

classes of prey were used because maximum capture

distance (and therefore the volume of water scanned bya drift-feeding fish) increases with prey size. A more

detailed description of the method for calculating the

size of the drift foraging window is presented in

Hughes & Dill (1990). Total energy available to a fish

at a given focal point was calculated by multiplying

total water volume by drift concentration (CONCb,mg m)3) for each size-class of invertebrate. Inverte-

brate biomass was converted to energy content using a

factor of 5200 Cal (21 790 J) g)1 dry weight (Cummins

& Wuycheck 1971).

Within the reactive distance of a fish, capture success

(CS) of prey decreases with increasing current velocity

and lateral distance of prey from the focal point of the

fish, and increases with fish size (Flore, Keckeis &

Schiemer 2001; Grossman et al. 2002). Data from Hill

& Grossman (1993) were used to develop piecewise and

logistic regression models of capture success using

these variables (Table 1; Fig. 1). The piecewise regres-

sion was used because it explained a slightly larger

proportion of the variance in the original data

(R2 = 0.92 for the piecewise regression, R2 = 0.88

for the logistic regression). Energy intake was adjusted

for capture success by multiplying the volume of water

passing within the reactive distance of a fish by the

associated capture success probability (CS, range of01) for each 20-cm interval on either side of a fish s

focal point. Energy intake was then expressed as

EI R3

i1RCAi V CSi CONCb 21790 J g

1

0:6 3600 106 1

Maximum daily consumption (g g)1 d)1) of fish was

calculated according to

MDC 0:303mass0:275 KA KB 2

as described in Hewett & Johnson (1992) with param-

eter values for coho salmon (Stewart & Ibarra 1991;

Table 1).

A modified Holling Disc function (after Hughes

et al. 2003) was used to model the probability of troutmissing prey because of handling effects (i.e. unde-

tected prey drifting past the focal point when trout

were intercepting another prey item). To correct for

this effect at higher prey densities, energy intake

(equation 1 above) for each size class of prey was

multiplied by the scalar HD.

HD 1=1 ER HTi 3

where ER (encounter rate) is the number of prey per

second passing through the reactive window of a fish

and HTi is handling time for a fish striking a prey itemfor each of the three (i) size classes of prey, calculated

after Hayes, Stark & Shearer (2000) as the sum of the

time required to intercept the prey and the time

required to return to the focal point (Table 1).

Swimming costs (inclusive of basal metabolism)

were calculated as an exponential function of fish

weight (g), fork length (cm) and focal velocity accord-

ing to Hughes & Dill (1990)

SC 10CMV 19mass 103 TS 4

based on data for sockeye salmon at 10 C (from Brett

& Glass 1973), where C = 2.070.37 log(length) and

M = 0.0410.0196 log(length) (Hughes & Dill 1990).

This swimming cost function likely underestimates the

true cost of swimming in turbulent flow under natural

conditions because it is based on forced swimming of

fish in laminar flow (Enders, Boisclair & Roy 2003). A

turbulence scalar (TS; Table 1, equation 4; Rosenfeld,

Leiter, Lindner & Rothman 2005) was included to

make swimming cost estimates more realistic by fitting

a positive exponential function to empirical data from

Enders et al. (2003), assuming that variation in velocity

at a focal point was equivalent to 33% of the mean

focal velocity (the average turbulence used by Enderset al. (2003)). Although fish will make use of velocity

refuges to minimise swimming costs and maximise

water volume searched (Hayes & Jowett 1994; Rails-

back & Harvey 2002), the species modelled (juvenile

cutthroat trout and coho salmon) tend to occupy focal

locations in the water column in relatively slow velocity

habitat, rather than holding in velocity refuges closer to

the substrate like species adapted to higher velocity

habitats [e.g. juvenile Atlantic salmon, Salmo salar L.,

and steelhead Oncorhynchus mykiss (Walbaum)].

Including the capture success function and a scalar

J . S . R O S E N F E L D & J . T A Y L O R20 4



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Table 1. Relationships and parameter values used in the drift-foraging and bioenergetic model

Parameter Units Value Equation Reference

Energy intake

CS Piecewise regression Estimated from graphical

data in Hill & Grossman

(1993)

if V < 15: CS = 1.02)(0.00634 V))

(0.00135 T)+(FL 0.00074 V))(V 0.0004 d)

if V > 15: CS = 1.04)(0.0131 V))(0.038 d)+

(0.00567 T)+(FL 0.00119 V))

(V 0.00144 d)+(d 0.00478 FL)

if V 15 and V 20 then average of functions

above

Logistic regression

CS eu

1eu , where

u = 1.28)0.0588 V+0.383 FL)0.0918 (d/RD))0.210

V (d/RD)

RD cm 12 Prey length (1)e()0.2 FL)) Hughes & Dill 1990

MCD cm (RD2)(V RD/Vmax)2)0.5 Hughes & Dill 1990

ER prey s)1 ER = CAi V CONCn Hughes 1998

HTi s HTi = (0.5 MCD/Vmax)+(0.5 MCD/(Vopt+Vfocal)) Modified from Hayes

et al. 2000

CONCn prey m)3

Foraging costs

Vmax cm s)1 Vmax = 36.23 FL

0.19 Hughes & Dill 1990

Vopt cm s)1 Vopt = 17.6 weight

0.05 Stewart et al. 1983

Vfocal cm s)1

TS TS = 10 [(0.06 V))0.98]+0.90 Rosenfeld et al. 2005

Metabolic costs

KA KA = (CK1 L1)/(1 + CK1 (L1)1)) Hewett & Johnson (1992)

KB KB = (CK4 L2)/(1 + CK4 (L2)1)) Hewett & Johnson (1992)

L1 L1 = e(G1 (T)CQ)) Hewett & Johnson (1992)

L1 L2 = e(G2 (CTL)T)) Hewett & Johnson (1992)

G1 G1 = (1/(CTO)CQ)) ln((0.98 (1)CK1))/(0.02 CK1) ) Hew ett & Joh nso n ( 1992)

G1 G2 = (1/(CTL)CTM)) ln((0.98 (1)CK4))/(0.02 CK4)) Hewett & Johnson (1992)

CTL 24 Hewett & Johnson (1992)

CTM 18 Hewett & Johnson (1992)

CK4 0.01 Hewett & Johnson (1992)

CTO 15 Hewett & Johnson (1992)

CQ 5 Hewett & Johnson (1992)

CK1 36 Hewett & Johnson (1992)

F F = FA TEMPFB e (FG p) Hewett & Johnson (1992)

U U = UA TEMPUB e(UG p) Hewett & Johnson (1992)

SDA 0.172 Hewett & Johnson (1992)

p EI /MDC Hewett & Johnson (1992)

FA 0.212 Hewett & Johnson (1992)

FB )0.222 Hewett & Johnson (1992)

FG 0.631 Hewett & Johnson (1992)

UA 0.0314 Hewett & Johnson (1992)

UB 0.58 Hewett & Johnson (1992)

UG )0.299 Hewett & Johnson (1992)

Estimation of growth rate

ED J g)1 wet weight ED = (386.7PDM))3632 Hartman & Brandt (1995)

PDM % If FL < 6: PDM = 0.18 Post & Parkinson (2001)

If FL 6 and FL 10 then

PDM = (0.068 + 0.018 FL)

If FL > 10 and FL 16 then

PDM = (0.171 + 0.007 FL)

If FL > 16 then PDM = 0.283




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for swimming costs in turbulent flow greatly reduced

bioenergetic estimates of growth, particularly in higher

velocity microhabitats; the model produces gross over-

estimates of growth rate if these functions are absent.

Foraging costs were modelled following the method

described in Hayes et al. (2000), where costs of

swimming at maximum speed (Vmax; Table 1) were

experienced for the duration of time required for the

fish to intercept the prey item, and the return speed to

the focal point was assumed to be the optimal

swimming velocity for the fish (Vopt). Maximum speed

during prey attack was multiplied by 0.59 since Hughes

et al. (2003) observed brown trout striking prey at this

average fraction of Vmax. Swimming costs for the

duration of a strike were calculated using bioenergetic

equations from Elliott (1976) for brown trout, Salmo

trutta L. Swimming costs for the attack portion of thestrike were multiplied by a factor of five to account for

the underestimation of active swimming costs by

forced swimming models (Boisclair & Tang 1993;

Hughes & Kelly 1996), as calculated using equation 4.

Costs of egestion (F) and excretion (U) were

calculated according to equations from Hewett &

Johnson (1992, after Elliott 1976) including a correc-

tion for the effects of ration size on F and U with

parameter values for coho (Table 1). Net Energy

Intake (NEI), the energy available for growth, was

calculated as gross energy intake less the costs of

egestion, excretion, specific dynamic action (Jobling

1994) and swimming costs:

NEI GEI1 F1 U SDA SC

Net Energy Intake was converted to growth incre-

ment using a generalised energy (J) to biomass (dry

weight) conversion relationship for the family Salmon-

idae (Hartman & Brandt 1995), where energy density

(ED) of tissue is an increasing function of percent dry

mass (Table 1). As percent dry mass (PDM) of juvenile

salmon increases with fish size (smaller fish have a

higher water content), percent dry mass of body tissue

was estimated using a piecewise regression (Table 1) fit

to data from Post & Parkinson (2001).

Bajer et al. (2003) showed that bioenergetic models

may systematically overestimate growth rates as alinear function of consumption. As consumption

increases, assimilation efficiency should decline (Ursin

1967; Jobling 1994) because of decreased residence time

of food in the gut and there should be associated

changes in the metabolic costs of specific dynamic

action, egestion and excretion (Bajer et al. 2003).

Deviations between actual and modelled growth at

high consumption arise from errors in estimates of

metabolic parameters [possibly because of the limited

range of conditions when originally measured in

laboratory experiments; (Bajer, Hayward, Whitledge

& Zweifel 2004a; Bajer, Whitledge & Hayward 2004b)].

Bajer et al. (2003) recommended regressing growth rateerror (EGR = modelled growth)observed growth) on

consumption to generate a relationship to correct for

this structural error in bioenergetic models when a

positive correlation between model error and consump-

tion is known to exist. This method is a form of model

fitting and should be viewed as a pragmatic approach to

correcting model error until more accurate bioenergetic

parameter estimates are derived (Bajer et al. 2004b).

To determine whether data exhibited a positive

relationship between growth rate error and consump-

tion, the error in modelled growth rate (EGR) was

regressed against consumption (estimated with theforaging model) for both cutthroat trout and coho. A

significant relationship was found for both species, and

therefore used to correct for bias in modelled growth

with increasing consumption as suggested by Bajer

et al. (2003), i.e. EGR was subtracted from modelled

growth to generate predicted growth.

Model calibration

The bioenergetic model was calibrated by comparing

observed and modelled growth of individual fish from

20

40

60

80

100

40

30

20

10

0

0

20

40

60

80

100

0 5 10 15 20

Lateral distance from focal point (cm)

Capturesuccess(%)

7 cm FL

11 cm FL

40

30

20

10

0

Figure 1. Capture success of rainbow trout feeding on invertebrate

drift as a function of lateral distance of prey from the focal point of a

fish, for 7 cm and 11 cm FL rainbow trout at current velocities ranging

from 040 cm s)1 (based on data from Hill & Grossman 1993).




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two separate experimental data sets, one for juvenile

cutthroat trout and the other for juvenile coho salmon.

Both species are anadromous, with adults maturing in

the ocean while juveniles rear in streams. The trout

calibration data set was based on observed growthrates of individual young-of-the-year (YOY, 5.0 cm

average FL; n = 8) and yearling and older fish

(13.7 cm average FL; n = 14) confined to discrete

pool or riffle habitats for 2530 days in Husdon Creek

on the Sunshine Coast of British Columbia (Rosenfeld

& Boss 2001). Husdon Creek is a small (3 m bankfull

channel width) coastal stream typical of the habitat

where juvenile anadromous cutthroat trout and coho

rear (combined juvenile salmonid density of 1.1

fish m)2; Rosenfeld, Porter & Parkinson 2000). Day-

time invertebrate drift (referred to as ambient drift in

subsequent modelling) was measured in 15 replicatepool and riffle habitats over 2 days in the middle of the

experiment (Rosenfeld & Boss 2001). Invertebrate drift

as well as microhabitat observations on fish focal point

depth, velocity and cross-sectional area through the

focal point were used as inputs for modelling growth

rate potential of individual fish in pool and riffle

enclosures (see Rosenfeld & Boss (2001) for more

detail). This permitted fitting of the model to observed

growth rates of two size-classes of juvenile trout

occupying different habitat types (pools and riffles) in

a natural stream.

The juvenile coho salmon calibration data set was

based on observed growth rates of dominant fish

reared in experimental stream channels at different

densities (2, 6 and 12 fish m)2; Rosenfeld et al. 2005).

In this experiment, young-of-the-year coho (5.1 cm

average FL; n = 12) were confined to experimental

stream channels and growth was measured along an

experimentally created gradient of natural invertebrate

drift abundance (0.0470.99 mg m)3). Only the growth

rates of dominant fish were modelled in each channel

(n = 12) because of the difficulty in estimating drift

abundance for subdominants (due to upstream prey

consumption). To increase the range of consumption

and growth levels over which the model could bevalidated, growth data from four sub-dominant fish in

the highest density treatments that achieved the lowest

growth in these experiments were included (average

growth of )1.0 0.2% per day; similar to a maxi-

mum daily weight loss for juvenile trout of 1% used in

an earlier bioenergetics model; Clark & Rose 1997).

These sub-dominant fish, which were located down-

stream of at least six to nine conspecifics over a

distance of 50 cm, were assumed to have an effective

energy intake close to zero. It was also assumed that

juvenile cutthroat trout would have similar negative

growth rates at zero energy consumption and these

four data points were included when fitting the

cutthroat trout growth rate errorconsumption rela-

tionship.

Fish were assumed to forage only during the day,based on observations of fish occupying quiescent

microhabitats with low water velocity at night. Noc-

turnal metabolic costs were therefore calculated

assuming a focal velocity of 0 cm s)1 (i.e. basal

metabolism only). Water temperature was fixed at

10 C for all modelling, close to the average temper-

ature in both experiments. Fit of both models was

assessed in terms of the proportion of variance in

observed growth rate explained by modelled growth

(R2 = SStot)SSerror/SStot).

To highlight the potential limitations of correcting

for modelled growth rate error (EGR) based onestimated (as opposed to known) consumption, the

relationship between EGR and consumption was cal-

culated using: (1) dominant coho from all 12 channels;

and (2) a subset of channels (n = 8) that excluded

coho in the lowest density treatment. Behavioural

observations of solitary juvenile coho in the lowest

density treatment (n = 4 channels) indicated that

solitary coho foraged more timidly alone than in the

presence of conspecifics, i.e. generally exhibited greater

refuging behaviour and did not strike prey to the limit

of their reactive distance. Modelled prey consumption

should therefore exceed actual consumption for soli-

tary fish, biasing the model-error consumption regres-

sion and causing an underestimate of corrected

growth.

Model performance was also assessed by compar-

ing predicted growth of YOY at satiation to the

expected range of maximum YOY growth rate for

similar-sized salmonids ($56% per day, e.g. Postet al. 1999) to evaluate whether the EGR correction

with or without solitary fish systematically underes-

timated predicted growth.

Quality of different habitat types as a function

of fish size and prey abundance

Influence of habitat on growth rate potential. Streams

are characterised by discrete habitat types with

different depth and velocity distributions (e.g. pools

and riffles; Petersen & Rabeni 2001). Changes in

growth rate potential along a gradient of increasing

habitat depth (e.g. from riffles to pools) were evaluated

by using habitat-specific velocities and depths as input

parameters to the bioenergetic model. As part of an

earlier study on hydraulic conditions in Husdon Creek,

depth and water velocity (at 60% of total depth) were




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measured at 20-cm intervals on multiple transects

spaced 20 cm apart in five replicate riffle, run, glide and

pool channel units in Husdon Creek (equivalent to

measuring velocity and depth at the nodes of a 20-cm

square grid superimposed on each habitat unit).Velocity and depths were measured at summer low

flow; channel unit types were differentiated as

described in Rosenfeld et al. (2000). Channel unit

lengths ranged from 1.1 to 9.2 m, with 67435 paired

velocity and depth measurements in each channel unit

(Table 3).

These habitat data and summer low-flow inverte-

brate drift concentrations from Husdon Creek were

used as inputs to the bioenergetic model described

above to calculate growth rate potential at each grid

point in each replicate channel unit. Growth was

modelled separately for YOY (5 cm FL) and yearling(13.7 cm FL) trout. The proportion of channel unit

surface area predicted to generate positive growth

(hereafter referred to as useable foraging habitat) was

calculated, and a mean calculated for each of the four

habitat types (n = 5 replicate channel units for each

habitat type). Average and maximum growth rate

potential were also estimated for each channel unit

(total n = 20) within the subset of area predicted to

generate positive growth (not for the entire habitat

unit, because absolute predictions of energy loss rates

in highly unsuitable habitats (e.g. at V = 60 cm s)1)

could not be validated). Average and maximum

growth rates were then calculated for each habitat

type (n = 5 replicate channel units per type).

The volume of water that flows past the focal point

of a drift-feeding fish is influenced by both water depth

and velocity, so that it is difficult to separate their

independent effects on habitat quality. To understand

how stream depth affects energy intake independent of

velocity, and to test the prediction that smaller fish can

obtain sufficient energy for growth in shallower water

than larger fish, prey consumption for YOY and

yearling trout was modelled along a simulated depth

gradient from 10 to 100 cm. Modelling assumptions

included a constant current velocity of 10 cm s)1

,summer low-flow drift concentration from Husdon

Creek, and an infinitely wide stream channel (i.e. a

foraging window width equal to the full reactive

distance of the fish).

Influence of prey abundance on growth rate

potential. The effect of prey abundance (a correlate

of ecosystem productivity) on habitat quality in

different habitat types was assessed by modelling

growth rate potential for 5 cm and 13.7 cm juvenile

trout over a gradient of increasing drift concentrations

set at 0.25, 0.5, 1, 2, 3, 4 and 5 times the ambient

baseline drift measured in Husdon Creek at summer

low flow during our growth rate experiment, as

described above.

Joint effects of percent pool and prey abundance

on reach-scale habitat quality. To determine the

relative effects on habitat quality of simultaneously

changing habitat and prey abundance at a scale larger

than a single channel unit, virtual stream reaches 20

channel units long were assembled by randomly

selecting pools, riffles, runs and glides from the five

replicates of each that were measured in Husdon Creek.

Habitat units were sampled in frequencies that

generated pool habitat ranging from 10, 25, 40, 55 and

70% of channel area at drift concentrations of 0.25, 0.5,

1, 2, 3, 4 and 5 times the ambient measured in HusdonCreek at summer low flow. The relative proportions of

habitats other than pools were fixed at the natural

frequencies observed in Husdon Creek (glide:run:riffle

ratio of 3:1:8). One thousand randomly assembled

reaches (average length 77 m, average wetted width

1.9 m) were generated for each percent pool and drift

combination for both 5 and 13.7 cm FL juvenile trout.

To assess trends in habitat quality along the drift

(productivity) gradient, reach average growth rate

potential in useable foraging habitat and percent of

habitat area with positive growth were plotted against

drift concentration for each pool frequency. The

independence of habitat and prey abundance effects

on habitat quality was determined by testing for an

interaction between percent pool and drift concentra-

tion.

Results

Correction of growth for consumption

Consistent with the observation of Bajer et al. (2003),

there was a significant positive relationship between

modelled growth rate error and consumption for both

juvenile cutthroat trout (P < 0.002, F1,6 = 25.4) andcoho salmon (P < 0.0001, F1,11 = 237 for all 12

channels, P < 0.002, F1,7 = 29.2 with low density

channels removed; Fig. 2, Table 2). Slopes of the

growth rate errorconsumption relationships (Fig. 2,

Table 2) were similar to those described for perch by

Bajer et al. (2003), as was the observation that the

bioenergetic model systematically overestimated

growth at high consumption and underestimated

growth at low consumption. However, growth rate

error appeared to be negligible at very low prey

consumption, i.e. the model accurately predicted the




8/17

negative growth of sub-dominant coho with extremely

low energy intake. Consequently, the growth rate error

data exhibited a curvilinear relationship close to zero

consumption; i.e. linear fits to the data would result in

the unrealistic prediction of positive growth at zeroconsumption. A polynomial regression was therefore

fitted to growth rate error at low consumptions (Fig. 2;

Table 3). Model error was significantly larger (more

negative) for YOY trout than for yearling and older

juveniles (P < 0.0001, F1,19 = 33.7). There was also

no significant relationship between growth rate error

and consumption for yearling and older juvenile trout

(P < 0.13, F1,12 = 2.7; Fig. 2). The minor difference

in mean predicted and observed growth (0.196%,

n = 14) was therefore used to model error correction

for yearling trout.

Including solitary fish from the low density channeltreatment increased the slope of the growth rate error

regression for coho (Fig. 2), indicating that the foraging

model overestimated consumption for solitary fish and

that predicted growth rates using an EGR correction are

sensitive to error in estimated consumption rates.

2

0

2

4

6

8

10

12

14

0 5 10 15 20 25

Growthrateerror(%day1)

Mean daily consumption (%day1)

Figure 2. Relationship between growth rate error (percent of body

weight per day) and mean daily consumption for uncorrected bioen-ergetic model predictions for juvenile cutthroat trout (black line; black

circles YOY; grey circles yearling fish) and coho salmon (open

diamonds. Solid grey line uses data for dominant fish from all stream

channels (n = 12), broken line is with solitary fish excluded ( n = 8);

see Methods for details).

Table 3. Average habitat characteristics (n = 5 replicates) of the channel unit types measured in Husdon Creek and used to assemble reaches

of different pool frequency

Habitat

Mean

area (m2)

Mean

depth (cm)

Maximum

depth (cm)

Mean

velocity

(cm s)1)

Maximum

velocity

(cm s)1)

Maximum

width (m)

Length

(m)

Grid

point

count

Pool 8.7 19.1 43.6 5.7 26.0 3.1 3.6 238

Glide 5.7 12.7 24.8 12.7 34.0 2.0 3.7 156

Run 2.97 10.7 18.8 15.2 42.4 1.5 2.7 86

Riffle 7.7 5.7 14.6 19.5 66.2 2.3 4.3 211

Table 2. Growth rate errorconsumption relationships observed in this study and Bajer et al. (2003)

Species

Weight

range (g)

Growth rate errorconsumption

relationship (%%) R2 Study

Coho salmon 0.62.6 If MDC 2% This study

(and Rosenfeld et al. 2005)EGR = 0.538 MDC)1.17a,b 0.96

EGR = 0.460 MDC)

0.86c

0.83If MDC < 2%

EGR = )0.70 MDC + 0.372

MDC2)0.027 MDC3)0.025

Cutthroat trout 1.22.1 If MDC 2.5% 0.71 This study

(and Rosenfeld & Boss 2001)EGR = 0.487 MDC)2.55

If MDC < 2.5%

EGR = )1.15 MDC + 29.46

MDC2)183.9 MDC3 + 0.00023

1641 EGR = )0.00196

Perch (Wisconsin model) 1830 EGR = 1.05 MDC)1.22 0.84 Bajer et al. 2003

Perch (KarasThoresson model) 1830 EGR = 0.61 MDC)0.88 0.72 Bajer et al. 2003

aAll dominant fish (n = 12).bEGR and MDC in %.cSolitary fish excluded (n = 8).




9/17

Evaluation of model performance

The bioenergetic model without any form of model

fitting explained 63% of the variance in growth of

juvenile cutthroat trout observed in Husdon Creek

enclosures (for YOY and yearling trout combined,

P < 0.0001, F1,20 = 64; Fig. 3a). When growth rate

was corrected for error associated with consumption

(using parameters and equations listed in Table 2),

the model fit improved considerably (R2 = 0.90,

P < 0.0001, F1,20 = 193; Fig. 3b). The slope of the

regression of observed vs modelled growth was not

significantly different from one (0.954 0.069 SE)

and the intercept was not significantly different fromzero (0.00028 0.00095 SE), indicating minimal

model bias.

Coho growth modelled over a larger range of prey

abundance did not fit the observed data as well as for

trout (Fig. 4a), although correcting growth rate

for consumption improved the model fit (Fig. 4b;

R2 = 0.42, P = 0.017, F1,10 = 8.2; R2 = 0.53,

P = 0.009, F1,9 = 10.9 with the single negative

growth outlier point in Fig. 4b removed). The slope

and intercept of the regression of observed on mod-

elled growth also approximated one and zero respec-

tively (0.80 0.24 SE for slope; 0.36 0.33 SE for

intercept).

Another criteria for effective model performance,

and in particular application of the EGR correction,

was whether maximum predicted growth rates were

realistic at prey availabilities greatly in excess of the

calibration data (e.g. ambient drift in Husdon Creek).

Maximum bioenergetic model predictions for YOY

growth at satiation were 5.8% for cutthroat trout at a

fork length of 5 cm in model simulations where

invertebrate drift was increased to very high concen-

trations (i.e. satiation). This is in the expected range for

maximum growth of YOY salmonids observed in the

wild (e.g. 56%; Post et al. 1999). Maximum bioener-

getic model predictions of YOY growth at satiation for5-cm coho were 3.1% with the EGR correction includ-

ing solitary fish and 4.7% using the EGR correction

excluded solitary fish; this is consistent with the

expectation of lower predicted growth rates with an

EGR correction based on overestimated consumption.

Influence of habitat on growth rate potential

For smaller fish, simulated energy intake along a

gradient of increasing depth asymptoted at approxi-

mately 60 cm (Fig. 5), as the increase in water volume

1.5

0.5

0.5

1.5

2.5

3.5

0 5 10 15

Fish length (cm)

Dailygrowth(%)

0.5

0.5

1.5

2.5

3.5

4.5(a)

(b)1.0

Figure 3. Observed growth (open circles) and model estimates of

daily growth (filled circles) for YOY (average 5 cm FL) and yearling

(average 13.7 cm FL) coastal cutthroat trout from Husdon Creek (data

from Rosenfeld & Boss 2001). Panel (a) shows unadjusted bioenergetic

model predictions; panel (b) shows bioenergetic model predictions

adjusted for systematic error associated with consumption.

1

0

1

2

3

4

0 0.4 0.8 1.2

Total drift concentration (mgm3)

Growth(%d

ay1)

0

2

4

6

8

10(a)

(b)

Figure 4. Observed growth (open circles) and bioenergetic model

estimates of growth (filled circles) for juvenile coho salmon trout feeding

over a gradient of natural drift abundance in experimental stream

channels (data from Rosenfeld et al. 2005). Panel (a) shows unadjusted

bioenergetic model predictions; panel (b) shows bioenergetic model

predictions adjusted for systematic error associated with consumption.




10/17

scanned for drift becomes limited by reactive distance

to prey rather than stream depth. By contrast, energy

intake for larger trout continued to increase with depth

because of their greater reactive distance. Yearling

trout were predicted to have a threshold depth of

17 cm below which energy intake was insufficient for

positive growth at ambient drift concentrations.

The distribution of growth rate potential in Husdon

Creek showed a similar pattern, with the highest

average and maximum predicted growth rate potential

in pool habitats and the lowest in riffles for both sizes

of trout (Fig. 6a, b); the proportion of habitat with

positive growth rate potential was also highest in pools

(Fig. 6c, d). Similarly, the proportion of a channel unit

with positive growth and average growth rate potential

both were increasing functions of average channel unit

depth for both YOY and older fish (Fig. 7). Young-of-

the-year were predicted to be able to use approximately

90% of a channel unit area at average depths in excess

of 1520 cm (Fig. 7). For yearling trout, there appears

to be a threshold average channel unit depth of 7.5 cmbelow which there are negligible microhabitats suitable

for growth, with the proportion of suitable habitat

increasing linearly with depth beyond this threshold

(Fig. 7).

Influence of increasing prey abundance on growth

rate potential

The extent of habitat predicted to generate positive

growth was sensitive to prey abundance. As prey

abundance increased, absolute increases in the propor-

tion of habitat suitable for growth tended to be higher

for yearling than YOY trout because more habitat was

suitable for YOY at all productivities (Fig. 6). The rate

of change in suitable habitat was steepest at drift

concentrations at or below the ambient observed in

Husdon Creek (i.e. the baseline level observed at

summer low flow), indicating that the extent of suitablehabitat is most sensitive to incremental changes in drift

at low productivities. The proportion of riffle habitat

suitable for growth of yearling trout was negligible at

or below ambient drift concentrations (Fig. 6d), indi-

cating that productivity-related thresholds are first

reached in shallower habitats and progressively deeper

habitats become energetic sinks as prey abundance

declines. Consequently, most of the useable habitat for

yearling trout is in pools at lower drift levels (Fig. 6d),

consistent with empirical observations (e.g. Rosenfeld

& Boss 2001).

0

50

100

150

200

250

300

0 10 20 30 40 50 60 70 80 90 100

Depth (cm)

GrossenergyIntake(Jh1)

Figure 5. Modelled estimates of gross energy intake (J h)1) for YOY

(filled circles) and yearling (open circles) cutthroat trout across a

gradient of increasing channel depth at a fixed current velocity of

10 cm s)1.

1

2

3

4

5

6(a) (b)

(c) (d)

Drift concentration (as multiples of ambient)

Growthratepotential

(%day1)

0

0.2

0.4

0.6

0.8

0 1 2 3 4 1 2 3 4 5

Proportionof

habitatwith

positivegrowth

ratepotential

5

0

Figure 6. Average and maximum modelled growth rate potential for

YOY (open circles) and yearling (filled circles) cutthroat trout in (a) pool

and (b) riffle channel units along a gradient of drift concentrations

(expressed as multiples of the ambient concentration measured in Hus-

don Creek at summer low-flow). Each point represents modelled meanvalues from 5 replicate channel units. The lower two panels (c and d)

illustrate increases in the average proportion of different habitat types

(pools black circles; glides white circles; runs grey circles; riffles

open diamonds) that generate positive growth rate potential for (c) YOY

or (d) yearling trout along a productivity gradient of increasing drift.




11/17

Modelled growth rate potential increased steeply

with elevated drift in both pools and riffles. Growth

rates of 13.7 cm FL trout were a linear function of drift

concentration (Fig. 6) because yearling trout were

below satiation at all drift levels. Maximum growth

rates of YOY plateaued at 5.8% per day at a drift

concentration of three to four times ambient in pool

habitat, although mean growth continued to increase

along the productivity gradient in both pools and riffles.

Growth rate also appeared to be most sensitive to

incremental increases in prey abundance at very lowproductivities. The steepest change in modelled growth

for YOY and yearling trout was at low drift concen-

trations, particularly in riffle habitats, which were

predicted to be unusable for yearling trout and marginal

for YOY growth at drift concentrations below ambient.

Joint effects of pool frequency and prey abundance

on reach-scale habitat quality

Simulations of reach-scale habitat quality showed

that increasing either the frequency of pools or drift

concentrations lead to systematic increases in average

growth rate potential (Fig. 8) and useable foraging

habitat (Fig. 9). Different combinations of pool fre-

quency and prey abundance are predicted to generate

similar reach-average habitat qualities. For example,

if a channel with only 10% pool habitat is interpreted

as degraded (Montgomery, Buffington, Smith,

Schmidt & Pess 1995; Johnston & Slaney 1996), then

increasing either pool frequency to 65% of wetted

area or tripling prey abundance will achieve similar

increases in reach-average growth rate potential

within useable habitat (Fig. 8, dotted line). However,

effects are not entirely substitutable, as increases in

pool area generally result in a greater incremental

increase in useable habitat than increases in prey

abundance.Incremental effects of prey enrichment on reach-

scale habitat quality are also greatest at low produc-

tivities (Figs 8 & 9). Increases in relative growth rate

are generally higher for YOY than yearling trout,

although the proportion of useable habitat plateaued

with increasing prey abundance for YOY but not older

fish (Fig. 9). A significant positive interaction between

percent pool and drift concentration on growth rate

potential (F1,34 = 17.8, P < 0.0002 for YOY,

F1,26 = 63.1, P < 0.0001 for 15 cm trout) indicated

that that the effects of habitat are synergistic with food

0

0.2

0.4

0.6

0.8

1(a)

(b)

Proportionofhabitatwith

positivegrowthrate

potential

0

0.5

1

1.5

2

0 5 10 15 20 25 30

Growthra

tepotential

(%day1)

Mean habitat unit depth (cm)

Figure 7. Proportion of habitat predicted to have positive growth

rate potential (a) and average channel unit growth rate potential (b) as a

function of mean channel unit depth for YOY (gray lines) and yearling

trout (black lines). Habitats in order of increasing depth are riffles (open

circles), runs (grey squares), glides (open triangles) and pools (black

circles).

1.0

2.0

3.0

0.0

0 1 2 3 4 5

Averagepositivegrowthratepotential

(%day1)

0.0

YOY trout (5 cm)

1+ trout (13.7 cm)


0.5

0. 0

1. 0

Figure 8. Reach-average growth rate potential for virtual reaches of

randomly selected channel units varying in proportion of pool habitatover a gradient of drift abundance (expressed as multiples of summer

low-flow drift concentrations in Husdon Creek) for YOY and yearling

trout. Solid circles are 10% pool, open circles are 25% pool, filled

squares and solid line are 40% pool (the average in Husdon Creek),

diamonds are 55% pool and squares are 70% pool habitat. Error bars

are omitted for clarity. The horizontal dotted line indicates the equiv-

alent effects on average growth rate potential (in useable habitat) of

tripling prey abundance in a reach with 10% pool vs increasing percent

pool from 10 to 65% in a stream with ambient drift levels.




12/17

resources, i.e. effects of increasing prey are greatest

when pools are abundant.

Discussion

Allometry and habitat quality for drift-feeding fish

Experiments and modelling demonstrated that physi-

cal habitat and prey abundance jointly limit growth of

drift-feeding fish through their concurrent effects on

energy intake. Drift concentration directly influences

prey encounter rate, while the velocity and depth

profile through the focal point of a fish determinescapture success and the volume of water searched for

prey. Changes in either habitat structure or drift

concentration can therefore be expected to simulta-

neously influence capacity of individual habitats or

entire streams.

Contrasting stream habitat types can be characteri-

sed in terms of both habitat quality (realised growth

rate potential in useable habitat) and habitat quantity

(area of habitat with positive growth rate potential, i.e.

useable foraging habitat for drift-feeding). Consistent

with expectations, average modelled growth rate

potential in the constituent habitat types of Husdon

Creek (pools, glides, runs and riffles) at low flow was

an increasing function of channel unit depth, as was

the proportion of each habitat type with positive

growth. This is consistent with numerous observationsof habitat use by drift-feeding fishes (e.g. Heggenes,

Northcote & Peter 1991), and indicates that higher

predicted energy intake in deeper habitats is sufficient

to account for differential use of pools, irrespective of

other demonstrated functions of pool habitat, such as

refuge from predation (Power 1984; Lonzarich &

Quinn 1995) or scouring flows (Quinn & Peterson

1996; Solazzi, Nickleson, Johnson & Rodgers 2000).

Modelling indicates that allometric changes in

absolute energy requirement also impose size-specific

constraints on habitat use. Smaller energy require-

ments allow YOY fish to achieve positive growth inshallow habitats that provide inadequate ration to

support larger trout at summer low flow, while growth

of yearling cutthroat under summer low flow condi-

tions was possible only in deeper pool habitat.

Although the results apply most directly to juvenile

salmonids, the general limitation of energy intake by

channel dimensions provides a clear mechanism not

only for preference of pool habitat but also for

downstream migration to deeper habitat as fish grow.

Based on optimal foraging theory, Bachman (1982)

(building on work by Kerr 1971) proposed that drift

abundance would limit fish growth and maximum size.

While bioenergetic modelling demonstrated this to be

true, energy intake and fish growth was also shown to

be limited by physical habitat dimensions when chan-

nel depth is less than the reactive distance of a fish a

common circumstance in small streams where maxi-

mum reactive distances range from 4580 cm, depend-

ing on fish and prey size (Hughes & Dill 1990).

Maintaining growth once channel dimensions con-

strain the drift-foraging window therefore requires

migration to deeper habitat, habitat with a higher drift

concentration, or habitat that supports a shift to

piscivory (Keeley & Grant 2001).

The allometry of energy requirement and swimmingperformance should also affect longitudinal trends in

habitat use along the River Continuum that result in

peak abundance of juvenile salmon in lower-order

streams (Mundie 1974; Rosenfeld et al. 2000). Small

physically complex streams in low-gradient landscapes

have lower velocities, so that a relatively high propor-

tion of habitat is within the performance capacity of

small fish while delivering sufficient energy for growth.

As streams increase in average depth and velocity

along a downstream continuum (Leopold & Maddock

1953; Rosenfeld, Post, Robins & Hatfield 2007), a

0

0.2

0.4

0.6

0.8

0

0.2

0.4

0.6

0.8

0 1 2 3 4 5

ProportionofhabitatwithpositiveGRP

YOY trout (5 cm)

1+ trout (13.7 cm)


Figure 9. Proportion of reach area with positive growth rate potential

for YOY and yearling trout over a gradient of drift concentration for

randomly assembled reaches varying in the proportion of pool habitat.

Symbol values as indicated in Figure 8 caption.




13/17

relatively higher proportion of habitat will be outside

of the performance capacity of small fish (e.g. YOY),

but a higher proportion should deliver sufficient prey

for growth of larger trout. As channel size and velocity

increase further, the relative proportion of habitatwithin the performance range of both YOY and

yearling trout should decline, leading to the expecta-

tion that channel-average densities of juvenile salmo-

nids should peak in smaller streams (Murphy, Heifetz,

Johnson, Koski & Thedinga 1986; Rosenfeld et al.

2000; Rosenfeld et al. 2007).

Effects of prey enrichment on habitat quality

Habitat use by drift-feeding fish is driven by the

allometry of swimming abilities as much as energy

requirement. Area of suitable drift-foraging habitatfor YOY did not appreciably increase beyond a

doubling of ambient drift concentration (Fig. 9),

indicating that as productivity increases, the distri-

bution of suitable habitat for YOY fish becomes

rapidly limited by swimming performance (inability

to use fast water habitat), rather than energy intake.

Further response to enrichment is through increased

growth rate potential in suitable habitat with minimal

increase in area occupied. By contrast, both growth

rate potential and area of usable habitat for larger

trout continue to increase along a drift concentration

gradient, indicating that distribution of older juvenile

trout in Husdon Creek was primarily limited by

energy intake rather than swimming performance.

However, increases in mass-specific growth rates were

much higher for YOY suggesting that smaller fish are

more likely to benefit from prey enrichment in small

streams (unless there is significant piscivory by

yearling trout).

Modelling also indicated that incremental changes in

prey abundance should have the largest impact in

oligotrophic streams. Similarly, as deep habitats are

the only ones that generate positive growth at low

productivity, incremental loss or restoration of deeper

pool habitat should also have the largest relative effectin oligotrophic streams. Implications for wild popula-

tions are that growth of juvenile salmonids will be

highly sensitive to natural seasonal and spatial varia-

tion in prey abundance as well as loss of deeper pool

habitat, particularly in low productivity systems (cf.

Grant et al. 1998).

Simulations suggested that similar target levels of

habitat quality can be achieved by increasing either

pool frequency or invertebrate drift. Implications for

habitat restoration are that increases in pool frequency

or prey abundance may be substitutable in terms of

their effects on habitat quality. While increased prey

alone permits exploitation of habitats that are bio-

energetically unavailable at lower productivities, simul-

taneously increasing drift and pool frequency is

predicted to be most effective at improving habitatquality, i.e. effects are predicted to be synergistic rather

than additive. However, the cost of instream rehabil-

itation measures (e.g. log or boulder placement to

increase pool frequency) may be much higher than

nutrient or salmon carcass addition, so enrichment

may be an appealing approach if funds are limited.

This inference must be tempered by consideration of

the additional functions performed by pools (e.g. cover

from predators, hydraulic refuges) that are not substi-

tutable through enrichment of shallow habitats.

Degraded channel structure (e.g. absence of large

woody debris and associated pools) will continue todepress habitat quality under enrichment and recovery

of natural riparian and watershed processes to increase

LWD inputs and natural pool-forming processes is

essential for restoring pre-impact capacity (Beechie &

Bolton 1999).

Model evaluation

Inclusion of key ecological processes absent in many

models resulted in more accurate growth estimates.

The most important of these is the capture success

function (derived from Hill & Grossman 1993), which

accounted for variation in prey capture as a function of

current velocity, fish size and distance of prey from the

focal point of the fish. This function greatly reduced

modelled energy intake, which is otherwise over-

estimated by a factor of two or more if fish are

assumed to consume all prey within their reactive

distance (Hughes et al. 2003; Piccolo, Hughes &

Bryant 2008a,b). Grossman et al. (2002) made a strong

case that the primary ecological factor limiting exploi-

tation of fast-water habitats was decreasing prey

capture success at higher water velocities, rather than

increased swimming costs. However, swimming costs

may rise more quickly with velocity than previouslythought because forced-swimming respiration models

that assume laminar flow underestimate the true costs

of swimming in turbulent water (Enders et al. 2003;

but see Nikora, Aberle, Biggs, Jowett & Sykes 2003 for

contrasting evidence). Regardless, inclusion of both

capture success functions (e.g. Van Winkle et al. 1998;

Nislow et al. 2000; Railsback & Harvey 2002) and

realistic swimming cost relationships are essential to

accurately model differential changes in energy intake

and expenditure along velocity gradients (Piccolo et al.

2008a).




14/17

The second key feature that contributed to a better

fit was correction for model error associated with prey

consumption as recommended by Bajer et al. (2003).

Systematic errors in estimated consumption from the

foraging submodel (from inaccuracies in the foragingmodel or error in estimated drift biomass) may also

have influenced either the slope or intercept of the

EGRconsumption relationship. However, the general

similarity of relationships from this study to those

observed by Bajer et al. (2003) suggested that the EGR

consumption relationship is primarily driven by errors

in metabolic parameter estimates rather than errors in

estimated consumption, and that the foraging model

does a reasonably good job of representing energy

intake and how it changes with habitat. Nevertheless,

inclusion of the model errorconsumption correction

factor represents a form of model fitting and the EGRcorrection applied in this study should not be viewed

as directly transferable to other species or populations

in different streams.

The application of bioenergetics to evaluate habitat

quality as presented in this study needs to be tempered

by several additional considerations. First, the effects

of predation risk were ignored. Predation risk alters

habitat use because fish trade off predation risk with

the need to grow sufficiently to survive and reproduce

(e.g. Fraser & Cerri 1982; Harvey 1991). Although

predation risk affects habitat quality, it remains useful

first to understand the effect of habitat on growth rate

independent of predation risk. Predation risk can then

be treated as an additional habitat attribute that

further modifies habitat quality based on survival,

which can be combined with growth rate potential to

generate an index of habitat quality that better reflects

fitness [e.g. the ratio of mortality risk to growth

(Gilliam & Fraser 1987; Bradford & Higgins 2001) or

the probability of a fish surviving to reproduce

(Railsback & Harvey 2002)].

Second, habitat quality was modelled as density-

independent growth rate potential, i.e. the growth that a

fish would realise in the absence of competition. Because

drift-feeding fish can locally deplete drifting prey (Leu-ng, Rosenfeld & Bernhardt 2009), this likely overesti-

mates growth in the presence of conspecifics (Hughes

1992). Although accounting for local prey depletion

would reduce estimates of growth rate potential, it

would be unlikely to alter the general conclusions or the

relative ranking of habitats in terms of quality. How-

ever, habitat capacity models based on drift-foraging

bioenergetics need to account for depletion and diffu-

sion dynamics of invertebrate drift (e.g. Hughes 1992) to

realistically predict fish abundance (e.g. Railsback &

Harvey 2002; Hayes, Hughes & Kelly 2007).

Finally, it is implicitly assumed that drift concen-

tration is independent of channel structure (e.g. the

proportion of pool or riffle habitat in a reach).

Changing the relative proportion of pool and riffle

habitat in a stream channel (as a modelling exercise, orthrough habitat restoration) will affect reach-scale

production of invertebrate drift as well as the distri-

bution of available habitat for fish. How the length,

frequency and juxtaposition of different channel units

affect the production and depletion of invertebrate

prey by drift-feeding fish is an area that demands

further consideration to assess their relevance to

instream production and restoration goals (Mundie

1974; Poff & Huryn 1998; Leung et al. 2009).

Despite these caveats, bioenergetic modelling is a

viable approach for assessing habitat quality for drift-

feeding fish (Nislow et al. 2000; Railsback & Harvey2002; Hayes et al. 2007). In combination with drift-

foraging models, bioenergetics provide an approach

for predicting the independent effects of prey abun-

dance and habitat structure on fish growth and

highlights the need to better understand how drift

abundance varies through space and time to influence

productive capacity. Bioenergetics also provides a

framework for understanding how the allometry of

energy requirements and metabolic costs, superim-

posed on the physical habitat template in streams,

jointly determine the distribution of useable habitat for

drift-feeding fish.

Acknowledgments

We would like to thank Shelly Boss, Taja Lee, Thomas

Leiter, Gerhard Lindner, Bob Little, Brent Matsuda,

Marc Porter and Michelle Roberge for assistance with

data collection and Dave Bates for help with site

selection and logistics. We also thank Cliff Kraft, Eric

Parkinson and Tom Johnston for reviewing an earlier

version of this manuscript. Funding for this research

was partially provided by Forest Renewal B.C., the

Habitat Conservation Trust Fund and Bugs Unlim-

ited. A spreadsheet version of the bioenergetic model isavailable on request from JSR.

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