Modelling available habitat versus availableenergy flux: do PHABSIM applications that neglectprey abundance underestimate optimal flows forjuvenile salmonids?

Jordan S. Rosenfeld and Ron Ptolemy

Abstract: Common applications of models to predict the response of fish habitat to altered stream flow (such as thePhysical Habitat Simulation Model; PHABSIM) assume that fish abundance is directly related to the area of suitablehabitat for limiting life stages and usually ignore flow effects on prey abundance. However, if prey availability is flowsensitive, then fish production may be more closely related to the total flux of available prey than to habitat area. Wecompared instream flow predictions from PHABSIM to predictions of optimal energy flux to drift-feeding juvenile cohosalmon (Oncorhynchus kisutch) estimated using a drift-foraging bioenergetics model. Flux of available energy to juvenilecoho salmon declined much more rapidly with decreasing flow than suitable habitat area estimated using PHABSIM, sothat, relative to the bioenergetic model, predictions from PHABSIM systematically overestimated productive capacity atvery low flows (i.e., underestimated the negative consequences of simulated water withdrawal). Applications of habitatsuitability based models like PHABSIM may systematically overestimate low-flow productive capacity for species thatprefer low velocities (e.g., pools) but are dependent on energy fluxes generated in higher velocity habitats (e.g., riffles).

Résumé : Les applications courantes des modèles de prévision de la réaction de l’habitat du poisson aux modifications dudébit d’un cours d’eau (tel que le modèle de simulation de l’habitat physique, PHABSIM) partent du principe quel’abondance des poissons est directement reliée a la superficie d’habitat convenable pour les étapes limitatives du cycle devie et ne tiennent généralement pas compte de l’effet de l’abondance des proies. Toutefois, si la disponibilité des proies estsensible au débit, la production de poissons pourrait être plus étroitement reliée au flux total de proies disponibles qu’a lasuperficie de l’habitat. Nous avons comparé des prévisions du débit minimum générées par PHABSIM a des prévisions duflux optimal d’énergie pour des saumons coho (Oncorhynchus kisutch) juvéniles se nourrissant d’organismes en dériveestimé a l’aide d’un modèle de bioénergétique de l’alimentation a la dérive. Le flux d’énergie disponible aux saumons cohojuvéniles diminuait beaucoup plus rapidement des suites d’une diminution du débit que d’une diminution de la superficiede l’habitat convenable estimée avec PHABSIM, de sorte que, comparativement au modèle bioénergétique, les prédictionsde PHABSIM surestimaient systématiquement la capacité productive a des débits très faibles (c.-a-d. qu’elles sous-estimaient les conséquences négatives du retrait simulé d’eau). Les applications de modèles comme PHABSIM reposant surl’habitat convenable pourraient surestimer systématiquement la capacité productive a faible débit pour les espèces quipréfèrent des faibles vitesses d’écoulement (p. ex. mouilles), mais qui dépendent des flux d’énergie générés dans leshabitats de plus grande vitesse (p. ex. bancs).

[Traduit par la Rédaction]

Introduction

Maintenance of aquatic biodiversity and ecosystem function aredependent on adequate stream flow, and human demands for waterand the goal of maintaining productive capacity of streams are inwidespread conflict (Postel et al. 1996; Poff et al. 2003). Manag-ing this conflict requires accurate tools to predict the impactsof flow alteration on aquatic life or to make informed tradeoffsbetween competing values. If the predicted biological conse-

quences of altered flow regimes are inaccurate then regulatorystandards may be ineffective, or negotiated trade-offs betweenthe costs and benefits of environmental protection will bedistorted.

Many approaches have been developed for modelling thebiological consequences of decreasing flow (reviewed inJowett 1997; Jowett et al. 2008; Moyle et al. 2011); a dispro-portionate number of these have focused on stream salmonids,because of their high value to society. A subset of instream

Received 8 March 2012. Accepted 10 September 2012. Published at www.nrcresearchpress.com/cjfas on 7 November 2012.J2012-0115

Paper handled by Associate Editor Michael J. Bradford.

J.S. Rosenfeld. Aquatic Science Section, British Columbia Ministry of Environment, 2202 Main Mall, The University of BritishColumbia, Vancouver, BC V6T 1Z4, Canada.R. Ptolemy. Aquatic Science Section, British Columbia Ministry of Environment, 2975 Jutland Road, Victoria, BC V8W 9M1, Canada.

Corresponding author: Jordan Rosenfeld (e-mail: Jordan.rosenfeld@gov.bc.ca).

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Can. J. Fish. Aquat. Sci. 69: 1920–1934 (2012) Published by NRC Research Pressdoi:10.1139/f2012-115

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flow techniques are rapid rule-of-thumb approaches based onqualitative or empirical relationships between fish habitat andbenchmark flows (e.g., the Tennant method; Tennant 1976) ormethods that focus on maintaining the natural variability of thehydrograph (Richter et al. 1997), which is well established asan evolutionary and ecological driver of stream communitystructure and function (Poff et al. 1997; Naiman et al. 2002;Lytle and Poff 2004). By far the most widely applied detailedassessment method for predicting the biological effects ofaltered stream flow are habitat suitability based models, likethe Physical Habitat Simulation Model (PHABSIM), whichmodel how availability of usable habitat for a target speciesresponds to incremental changes in discharge (Tharme 2003;Jowett et al. 2008). PHABSIM and its variants link a hydraulicmodel that predicts flow-related changes in velocity and depthto a biological model (typically habitat suitability curves),which predict the response of the target organism to localdepth and velocity.

PHABSIM is widely used because it provides a simplequantitative framework for generating estimates of availablehabitat at different flows, and the underlying logic of theprocess is clear and intuitively reasonable. However, the ap-plication of PHABSIM has received mixed reviews. Severalstudies have identified core conceptual or logistic problemswith the approach (Mathur et al. 1985; Castleberry et al. 1996;Kondolf et al. 2000), or have failed to show any relationshipbetween fish abundance and available habitat (e.g., Irvine et al.1987; Bradford et al. 2011). However, other studies haveshown that habitat availability models can reliably predict floweffects on habitat capacity; for example, a version ofPHABSIM (RHYHABSIM; Jowett 1989) has been regionallyvalidated for adult trout in New Zealand, where research hasdemonstrated that fish abundance is positively related to avail-able habitat (Jowett 1992; Jowett and Biggs 2006). Othershave also found that PHABSIM estimates of available habitatare a useful correlate of habitat availability in populationmodelling (Sabaton et al. 1997; Capra et al. 2003; Parra et al.2011), if not a direct predictor of fish abundance. More re-cently, Beecher et al. (2010) found that predictions of avail-able habitat generated with PHABSIM greatly underestimatedoptimal flow for juvenile coho salmon (Oncorhynchus kisutch)in western Washington, USA (where optimal refers to flowsthat maximize available habitat). In their study PHABSIMincorrectly predicted both increasing habitat availability withdeclining flows and optimal flows that were lower than thoseactually observed in their study stream, despite a strong pos-itive relationship between summer low flow and coho salmonsmolt production.

Systematic underestimation of optimal flows for juvenilecoho may indicate a fundamental problem with standard ap-plications of PHABSIM. Because habitat suitability basedmodels like PHABSIM infer a direct correlation betweenavailable habitat and productive capacity when habitat is lim-iting, bias may arise if there is a mismatch between flows thatmaximize available habitat versus those that maximize energyflow (or production) in available habitat (Shirvell 1986; Irvineet al. 1987; Wills et al. 2006). Like many lotic salmonids,juvenile coho feed on drifting invertebrates that are generatedprimarily in higher velocity riffles upstream of their preferredslow-velocity pool habitat (Pearson et al. 1970). As flowdeclines, delivery of invertebrate drift from the upstream riffle

may decrease much more quickly than the availability ofhabitat in pools (Harvey et al. 2006); in the extreme case ofzero flow, habitat will still be available in standing pools(Fig. 1), although energy flux of drifting invertebrates willapproach zero as riffles run dry (other than terrestrial drop;Nielsen 1992).

Despite invertebrate production in riffles being long recog-nized as particularly important to the production of pool-rearing fish (e.g., Pearson et al. 1970; Stalnaker and Arnette1976; Holtby and Hartman 1982), the potential for a mismatchbetween flows that maximize prey production versus availablehabitat (Fig. 1c) has been largely ignored in routine flowassessments, particularly in North America. Although the ini-tial intent of PHABSIM was to model flow effects on availablehabitat as part of a broader suite of attributes using the In-stream Flow Incremental Methodology approach (e.g., in con-junction with water temperature, quality, and food production;Orth 1987), the science underlying flow effects on drift pro-duction was never rigorously pursued in North America. Con-sequently, the majority of standard PHABSIM applicationscurrently focus on changes in available habitat and generallyneglect flow effects on prey abundance (with New Zealand asa notable exception; Jowett and Biggs 2006).

The specific objective of this study was to revisit thisissue and evaluate the potential for PHABSIM predictionsof available habitat to overestimate productive capacityalong a declining flow gradient relative to estimates ofavailable energy flux (invertebrate drift). To test this, weused invertebrate drift measurements and velocity– depthtransects collected from a small coastal salmonid stream asinput to both PHABSIM and a drift-foraging model toassess differences in available habitat versus available en-ergy flux along a discharge gradient. A secondary objectivewas to evaluate whether divergence between models wassensitive to the properties of the chosen habitat suitabilitycurves for juvenile coho. To this end we modelled availablehabitat using three different sets of published habitat suit-ability curves that differed in optimal velocity.

Materials and methods

Study siteHabitat data for modelling were collected from Husdon

Creek, a small coastal stream on the Sunshine Coast of BritishColumbia, Canada, where juvenile coho salmon (Oncorhynchuskisutch) and cutthroat trout (Oncorhynchus clarkii clarkii) rearat combined densities of �1.1 individuals·m–2. Husdon Creekdrains a 3.4 km2 watershed of second-growth conifer forest,has a mean bankfull channel width of 3.4 m, a summer lowflow of �0.02–0.03 m3·s–1 (20–30 L·s–1), and an estimatedlong-term mean annual discharge of around 0.25 m3·s–1. Sub-strate is dominated by gravel, with sand and cobble andabundant large wood (0.37 pieces of large wood per linearmetre) in a pool–riffle channel with a 1% gradient. HusdonCreek is typical of the smaller low-gradient pool–riffle streamswhere coho rear in relatively high densities (Rosenfeld et al.2000).

Velocity and depth data for habitat modelling were col-lected along transects in four different riffle, run, glide, andpool channel units (total n � 16), once in each unit duringsummer low flow (July 2001) and once during winter high

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flow (December 2001). These data were collected as part of abroader study to model changes in habitat quality and hydrau-lic attributes in different habitat types (Rosenfeld and Taylor2009; Rosenfeld et al. 2011). Depth and velocity (at 60% oftotal depth from the water surface) were measured at 20 cmintervals on multiple transects spaced 20 cm apart in eachchannel unit (essentially measuring velocity and depth at thenodes of a 20 cm square grid superimposed on each habitatunit) using a metre stick and a Marsh–McBirney model 2000flow meter. Velocity and depth data for PHABSIM modellingwere extracted from a subset of transects embedded in the datagrid. Channel unit lengths ranged from 1.1 to 9.2 m, anddischarge at sampling ranged from 0.02–0.03 m3·s–1 duringsummer low flow to 0.17–0.64 m3·s–1 at winter high flow, with0.64 m3·s–1 approaching bankfull stage. Time constraints pre-cluded collection of additional data at intermediate flows.

Each channel unit was classified during summer low flow asa pool (0% gradient, low current velocity, deep), glide(0%–1% gradient, slow current velocity, minimal surface wa-ter disturbance), run (1%–2% gradient, high current velocity,turbulent flow), or riffle (1%–3% gradient, high current veloc-ity, water surface broken by protruding substrata, shallow) asdescribed in Johnston and Slaney (1996) and Moore et al.(1997). Sampled channel units were dispersed throughout thereach (i.e., rarely contiguous) and were chosen to be represen-tative of their class; to simplify modeling, habitat units that didnot consist of a single channel-spanning habitat type werenot included. Selected channel units were not significantlydifferent in key attributes like length (analysis of variance,ANOVA, F[7,57] � 0.17, p � 0.68) or maximum depth(ANOVA, F[7,57] � 1.34, p � 0.25) from channel units of thesame habitat type in a larger sample of 38 channel unitsmeasured over a reach length of 50 bankfull channel widths,indicating that the selected units were representative of theirrespective habitat classes. The same channel units were mea-sured during winter high flow. Measured length and locationof channel units was fixed across high and low flows, butchannel width and the number of point measurements werehigher in winter because of elevated discharge.

PHABSIM habitat modellingSemipermanent stakes were used to locate transects grids.

Velocity and depth measurements for PHABSIM modellingwere used from three transects in each of the 16 focal habitatunits (i.e., a total of 48 transects over a combined lineardistance of �60 m distributed throughout 16 channel units).One transect was located near the centre of each channel unitand two other transects were located within the top and bottommetre of each channel unit (i.e., the head and tailout for poolhabitats). Transects were selected to capture the main variationin physical structure that drives variation in habitat quality inHusdon Creek; although random selection of transects couldhave achieved the same end, a larger number would likelyhave been required. Similarly, transects were collected fromthe four major habitat types to capture the range of variation inhydraulic conditions at the channel unit scale and to comparetrends in available habitat and available energy between hab-itat types. Water surface elevation at different flows wasestimated as the difference between the stream bed and thewater surface measured to the nearest centimetre with a metrestick at 20 cm intervals along each transect. Velocities weremeasured to the nearest centimetre per second with a Marsh–McBirney model 2000 flow meter. Bed elevation was assumedto be constant or to vary minimally between summer andwinter measurements. Husdon Creek is a stable channel, andthere were no signs of substantial scour or fill in the sampledchannel units between low- and high-flow sampling; we there-fore assume that any scour and fill would introduce randomerror rather than directional bias in the data. Using a metrestick to measure flow-related changes in water surface eleva-tion and depth is less accurate than using a level or installinga temporary staff gauge at sample sections. Although thissimpler approach likely introduces a greater degree of randomvariation (error) in depth measurements, the error is likely ofminor importance relative to variation in velocity and depthdriven by differences between channel unit types (e.g., poolsvs. riffles), between transects located in the deep centre versushead or tailout of pools, or even between transects randomlyplaced 30–50 cm apart.

Fig. 1. Schematic diagram illustrating the differential effects of a decline in flow from moderate (a) to zero discharge (b) on availablehabitat (solid line) versus the flux of energy to available habitat (broken line) (c). Greater sensitivity of energy flux to declining flows maycause a shift in optimal flows to higher discharge (broken arrow) or a more rapid decline in available energy flux with declining flowsrelative to available habitat (double-headed arrow).

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Available habitat was modelled with three different sets ofhabitat suitability curves representing a range of velocity pref-erences. All curves demonstrate a strong preference for slower(including zero) velocity habitats, but published curves fromthe Trinity River (Hampton 1988) are the most extreme, witha peak optimal velocity at 0 cm·s–1 (Fig. 2). Curves fromBeecher et al. (2002, 2010) are intermediate in velocity pref-erence and are considered the most robust because they arewell documented and collected using the recommended sam-pling design. Curves from Ptolemy are based on detailed fishdensity assessments coupled with stream transects across awide range of stream sizes throughout British Columbia(R. Ptolemy, unpublished data) and increase the habitat suit-ability range at intermediate velocities and greater waterdepths (Fig. 2). Available habitat generated by PHABSIMmodelling was expressed as weighted useable area (WUA; theproduct of area and habitat suitability value), which is thestandard output from PHABSIM. Juveniles of coho and otheranadromous salmonids often rear at high densities in smallstreams in western North America, where summer lowflows tend to be prolonged. Summer growth and survival isusually strongly density dependent at low flows, indicating

habitat limitation under normal recruitment levels (e.g.,Harvey et al. 2006; Beecher et al. 2010), supporting anexpectation of a positive relationship between WUA and ju-venile abundance at low summer flows.

We modelled velocities and depth using PHABSIM (IFG4computation procedure) with a high (0.17–0.64 m3·s–1) andlow (0.02–0.03 m3·s–1) calibration flow for each channel unit.Changes in habitat availability were simulated over a flowrange of 0.02–0.2 m3·s–1 by increments of 0.02 m3·s–1; mod-elled discharges were generally well within the range of cal-ibration flows, although often closer to the lower calibrationflow. Measured velocities ranged from –4 to 59 cm·s–1 at lowdischarge and –23 to 128 cm·s–1 at high discharge. Sixty-sixpercent of modelled velocities were within 2 cm·s–1 of ob-served velocities across all flows; 90% were within 7 cm·s–1;and 95% were within 10 cm·s–1. Deviation of modelled watersurface elevations from observed elevations at calibrationflows averaged 0.5 cm, with a maximum deviation of 4 cm. Toeliminate deviations of PHABSIM velocity and depth predic-tions as a source of error in optimal flow estimation, wemodelled available energy flux in each channel unit usingpredicted (rather than observed) velocity and depth data.

Fig. 2. Velocity and depth habitat suitability curves for coho salmon from three different sources (Trinity River, Beecher, Ptolemy) thatwere used in habitat modelling.

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Modelling available energy fluxJuvenile trout typically hold at a focal point in a stream and

forage on invertebrate prey drifting through their visual field.We modelled energy intake using a modification of the drift-foraging model described by Hughes and Dill (1990) by in-cluding a capture success function to more realistically lowerprey intake with increasing water velocity and lateral dis-tance of prey from the focal point (Hill and Grossman 1993;Grossman et al. 2002; see Rosenfeld and Taylor 2009 for adetailed description). Although this capture success function isbased on data for rainbow trout (Hill and Grossman 1993), itis appropriate for coho since Piccolo et al. (2008) recentlyshowed no detectable difference in capture success betweenjuvenile coho and rainbow trout. Available energy per unittime at a given focal point was then calculated as the volumeof water passing through the reactive field of a fish multipliedby invertebrate drift concentration and capture success (rang-ing from 0% to 100%). We used mean summer invertebratedrift concentrations reported for Husdon Creek (Rosenfeld andBoss 2001), the same stream where velocity–depth data werecollected.

To characterize the available energy flux in a channel unit,we calculated the available invertebrate drift flowing throughthe central transect of each unit, which was chosen because itwas most likely to be representative of each habitat (i.e., thecentral transect of a pool will include the deepest habitatpreferred by coho). Each transect was divided into 20 cm widecells associated with each depth and velocity measurement.The drift-foraging model was first used to calculate net energyintake for a focal point in the centre of each 20 cm wide cellacross each transect as described in Rosenfeld and Taylor(2009); for consistency of modelling, we assumed a focal pointat 60% of total cell depth. Drift energy was assumed to beunavailable in cells where coho were predicted to have nega-tive growth (i.e., where swimming costs exceeded energyintake). Available energy flux across a transect was thereforecalculated as the sum of discharge through cells with positivegrowth (henceforth referred to as “occupied” cells) multipliedby invertebrate drift concentration and capture success at themean velocity of occupied cells, expressed in units of joulesper hour. To characterize potential energy expenditures of fishat different flows, we also estimated swimming costs as afunction of velocity at the inferred focal point in occupiedcells, including the incremental costs of central place foragingat 12 °C as described in Hughes and Dill (1990) and Rosenfeldand Taylor (2009).

For modelling scenarios, we assumed that the concentrationof drifting invertebrates in the water column (numbers andbiomass per cubic metre) did not change with discharge. Wemade this assumption because (1) there is insufficient quanti-tative information on how drift changes with discharge tosupport any other relationship, and (2) this assumption servesas a starting point for understanding the origin of any discrep-ancies between flows that maximize available habitat versusflows that maximize energy flux. We briefly consider theconsequences of assuming that drift is flow-invariant in thediscussion. Although there is some evidence for a decline ininvertebrate drift concentration as summer low flow pro-gresses (e.g., Steingrímsson and Grant 1999; Sotiropouloset al. 2006), and some expectation that drift will decrease atlower flows as area and velocity of wetted riffle decline (e.g.,

Annear et al. 2004; Hakala and Hartman 2004; Harvey et al.2006), effects of flow reductions on drift are inconsistent(Dewson et al. 2007; Bradford and Heinonen 2008) so thatquantitatively exploring these relationships in simulationswould be speculative.

Data analysisData analysis focused on determining (1) whether there

were systematic differences in estimates of available habitat(WUA) or optimal flows calculated with PHABSIM relative toavailable energy (henceforth referred to as “relative error”), ateither the channel unit (pool–riffle) or reach scales (100 se-quential habitat units in a virtual reach) and (2) the sensitivityof any relative error to differences in either channel structure(in terms of percent pool habitat in a reach) or habitat suit-ability curves.

Flow-related changes in available energy flux (J·h–1) oravailable habitat (WUA) were assessed by plotting themagainst discharge by habitat class (pools, glides, runs, orriffles). Differences in overall energy flux and WUA betweenhabitat classes were assessed using ANOVA. To evaluatewhether habitats differed in sensitivity to changes in dischargeat very low flow (double-headed arrow in Fig. 1), the rate ofchange in available energy or WUA at low flow was charac-terized in terms of the initial slope of the WUA–dischargecurve for individual channel units at low modelled flows(between 0.02–0.06 m3·s–1). To correct for differences inslope associated with different energy and WUA scales, datawere first standardized to a maximum of one by dividing allenergy or WUA values by the maximum observed in eachmodelling scenario associated with different habitat suitabilitycurves. Because parametric assumptions could not be met,differences in initial slope among habitats (n � 4) and models(n � 4; energy flux, and WUA with Trinity, Beecher, orPtolemy suitability curves) were compared using a Kruskal–Wallis test.

To test for flow-related trends in the relative error betweenWUA estimates and available energy (which are expressed indifferent units), and to control for differences in maximumcapacity among channel units, energy and WUA versus dis-charge curves for each channel unit were first standardized toa maximum of one as described above (i.e., by dividing valuesat different flows by the maximum observed in that channelunit). Standardizing curves is consistent with general instreamflow modeling approaches, since guidelines for interpretationof WUA versus discharge curves emphasize that differences inthe absolute value of WUA between streams may be difficultto interpret, but the shape of the WUA–discharge curve andthe location of the peak should be robust regardless of theinterpretation of a unit of WUA. Standardizing curves allowedus to calculate relative error of the WUA estimate (relative toenergy flux) by subtracting standardized WUA from the stan-dardized estimate of energy flux at each flow for the samechannel unit. Note that for the purposes of calculating relativeerror we treat trends in available energy flux with flow as theleast biased measure of habitat capacity or productivity and theWUA estimates as being in error. The rationale for this is thatthe bioenergetics drift-foraging model should provide themore accurate estimate of habitat capacity because it accountsfor flow-related changes in both available habitat and energyflux and has been calibrated against observed growth rates of

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trout in Husdon Creek (Rosenfeld and Taylor 2009). Mostimportantly, unlike the frequency-based habitat suitabilitycurves typically used in PHABSIM modelling, bioenergeticpredictions provide a more objective assessment of productivecapacity (i.e., growth rate potential) that controls for density-dependent and territorial effects that often cause habitat usecurves to poorly represent true habitat quality (Van Horne1983; Rosenfeld et al. 2005).

Habitat-specific differences in relative error of WUA esti-mates were visually assessed by plotting relative error versusdischarge with 95% confidence intervals. Optimal flows forindividual channel units (n � 16) were determined as the flowthat maximized either WUA or energy flux, and differences inoptimal flows among habitat types were assessed using two-way ANOVA with habitat (four levels) and model (four levels;energy flux, and WUA with three sets of suitability curves) asfactors. Modelled consumption efficiency of drifting energyflux was calculated for each channel unit as the availableenergy flux (the product of discharge, drift concentration, andcapture success in occupied cells) divided by the total energyflux (drift concentration � total discharge in the cross-section). Flow-related trends in consumption efficiency ofdrifting energy flux were assessed by plotting consumptionefficiency versus discharge for different habitat classes.

To assess the potential for divergence between PHABSIMestimates of available habitat and available energy at the largerreach scale, and to evaluate the influence of channel structure(percent pool habitat) on optimal flows, we created virtualstream reaches by selecting random combinations from theoriginal 16 channel units and concatenating them into reachesof 100 channel units in length. We repeated this bootstrapprocedure 1000 times with replacement at discharges rangingfrom 0.02 to 0.2 m3·s–1 to generate mean changes in WUA andavailable energy across a discharge and channel structuregradient. The frequency of selection of different channel unittypes was varied to create virtual bootstrap reaches of 10%,40%, 70%, and 90% pool habitat.

Note that our modelling of available energy flux at thechannel unit scale does not account for potential feedbackbetween drift foraging and drift supply. However, salmonidscan reduce drift concentration through consumption (Hughes1992; Hayes et al. 2007), and this effect is likely to be mostsignificant when discharge is low and capture efficiency ishigh (e.g., Leung et al. 2009). To partially account for theeffects of fish depletion of drift at the reach scale, and toexplore how this may affect assessment of optimal flows, weallowed drift flux exiting a channel unit to be depleted by theavailable energy flux (consumption) estimated using the drift-foraging model for each upstream channel unit and discharge,so that the drift flux entering each downstream channel unitwas depleted in proportion to the capture efficiency in theupstream channel unit.

Realized drift concentrations in streams represent an equi-librium between entry and exit of benthic invertebrates fromthe water column (Rosenfeld and Raeburn 2009); we thereforeused a simple mass-balance approach for modelling drift re-generation between sequential adjacent channel units. Wemodelled drift regeneration assuming that entry (production)and exit of invertebrates from the drift is in equilibrium oncedrift concentrations asymptote to a maximum value and thatrealized drift concentration is a function of mean residence

time in the water column (or drift distance) of invertebrates(Elliott 2002). We assumed a constant 10% per linear metreregeneration of drift flux asymptoting at the original driftconcentration, equivalent to assuming mean drift distances of10 m in riffles, which is typical for riffle habitat in smallstreams (Elliott 2002; Hansen and Closs 2007; Rosenfeld andRaeburn 2009). We assumed a drift-flux regeneration of 5%per linear metre in nonriffle habitats (implying a lower rate ofdrift production). While this represents a very simple mass-balance approach to modelling drift regeneration, it capturesthe basic dynamics of drift depletion and generation suffi-ciently realistically to assess how they might interact withdischarge to influence assessments of optimal flows. Althoughmore complicated data-intensive approaches are available formodelling drift transport (e.g., Railsback et al. 2003; Hayeset al. 2007), they make similar simplifying assumptions withrespect to parameterization of drift production.

Flow-related trends in reach-scale available energy andWUA were plotted against discharge for different channelstructures (i.e., 10%–90% pool), and relative error was calcu-lated by standardizing available energy and WUA to a maxi-mum of one within each channel structure – dischargescenario, as described earlier. Reach-scale divergence in stan-dardized WUA was plotted against discharge with 90% con-fidence intervals on bootstrapped distributions to assess thepresence and severity of relative error in optimal flows esti-mated using PHABSIM. Differences in optimal flows at thereach scale were assessed using two-way ANOVA with chan-nel structure (four levels) and model (four levels; energy flux,and WUA with four sets of suitability curves) as factors. Alldata analyses were carried out using SAS version 8.1 (SASInstitute Inc. 1989).

ResultsAlthough there was considerable variation among replicate

channel units (individual lines in Fig. 3), available energy fluxwas generally highest in pool habitat and lowest in runs andriffles (Fig. 3, far left panels; n � 16, F[3,15] � 9.5, p � 0.002),as expected based on bioenergetics and known preference ofjuvenile coho for pools (e.g., Glova 1986; Nielsen 1992;Rosenfeld et al. 2000). Despite Trinity, Beecher, and Ptolemyhabitat suitability curves exhibiting large differences in abso-lute WUA estimates (Fig. 3), relative rankings between habi-tats at low flow (0.02 m3·s–1) were similar across models andavailable energy (no significant habitat � model interaction;F[9,48] � 0.29, p � 0.98). Optimal flows (discharge at thehighest point in each curve; Fig. 1) were significantly lower inpools than in other habitats (Table 1; F[3,48] � 12.9, p �0.0001). Although there was a tendency for Trinity andBeecher habitat suitability curves to underestimate optimalflows in pools (Table 1; Fig. 3), and for optimal flows to beoverestimated in all other habitat types (Table 1), optimalflows did not differ significantly among habitat suitabilitycurves or available energy flux (F[3,48] � 0.85, p � 0.48). Ingeneral, most curves were relatively flat with very poorlydeveloped optima, with the exception of available energy inpool habitat and Trinity estimates of WUA in pools (Fig. 3).

Differences in slopes of curves at low flow were much morepronounced than differences in optimal flows, i.e., habitats andmodels differed greatly in their sensitivity to low flows(Fig. 3). The initial slope of the available energy curve was

Rosenfeld and Ptolemy 1925

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consistently steeper in pools than in other habitats (Fig. 3;Kruskal–Wallis test, �2 � 11.2, p � 0.011; relative slopes of0.91, 0.32, –0.05, and 0.02 in pools, glides, runs, and riffles,respectively), indicating a much greater sensitivity of produc-tive capacity to declining flows in pools. The slope of availableenergy in pool habitat was also significantly steeper than theslope of available habitat (WUA) modelled with all threehabitat suitability curves (Kruskal–Wallis test, �2 � 13.1,

p � 0.005; relative slope in pools of 0.90, –0.24, 0.09, and0.16 for available energy and Trinity, Beecher, and Ptolemyhabitat suitability curves, respectively). As a consequence,WUA estimates in pools were positively biased at very lowflows for all habitat suitability models (i.e., low-flow habitatquality in pools was significantly overestimated using PHAB-SIM; Fig. 4), while PHABSIM had a tendency to overestimateavailable habitat in other habitat types at higher flows (Fig. 4).

Fig. 3. Change in energy flux with stream discharge (first vertical set of panels) versus change in weighted useable area with discharge forTrinity, Beecher, and Ptolemy habitat suitability curves (last three vertical sets of panels). Horizontal rows represent model output for,respectively, pool, glide, run, and riffle habitats. Shaded bar represents the expected range of 20% mean annual discharge. Different linesrepresent replicate channel units.

0 0.1 0.20 0.1 0.2

Energyflux Trinity

0

50

100

150

200

0 0.1 0.2 0 0.1 0.2

Pool

Glide

Run

Riffle

1

2

1

2

1

2

1

2

0

50

100

150

200

0

50

100

150

200

0

50

100

150

200

Ava

ilabl

e e

nerg

y (J

· h-1

)

4

2

6

4

2

6

4

2

6

4

2

6

Beecher Ptolemy

5

10

5

10

5

10

5

10

Wei

ghte

d us

eabl

e ar

ea (W

UA

)Discharge (m3·s-1)

200

0.00.0 0.0 0.0

Table 1. Estimated discharges (Qopt) that maximize available energy versus weighted useable area (WUA) for different habitat suitabilitycurves as a function of habitat type (pools, glides, runs, and riffles) in Husdon Creek.

Qopt

pool (m3·s–1)�Qopt

pool (%)Qopt

glide (m3·s–1)�Qopt

glide (%)Qopt

run (m3·s–1)�Qopt

riffle (%)Qopt

riffle (m3·s–1)�Qopt

run (%)

Available energy 0.070 (0.03) — 0.120 (0.07) — 0.105 (0.07) — 0.155 (0.03) —WUA (Trinity) 0.035 (0.03) –50 (27) 0.150 (0.09) �25 (64) 0.200 (0.00) �90 (63) 0.180 (0.02) �16 (32)WUA (Beecher) 0.055 (0.01) –21 (36) 0.155 (0.02) �29 (92) 0.130 (0.09) �24 (83) 0.190 (0.02) �23 (16)WUA (Ptolemy) 0.085 (0.03) �21 (49) 0.145 (0.07) �21 (52) 0.135 (0.08) �29 (33) 0.175 (0.05) �13 (24)

Note: �Qopt represents the percent deviation of the optimal flow calculated using WUA from the optimal flow estimated using available energy.

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The overestimate of habitat quality in pools at low flows isnotable because pool habitat also has the highest overall pro-ductive capacity (Fig. 3), and relative error in pool habitattherefore has the greatest leverage on estimates of productivecapacity at the reach scale.

Modelled consumption efficiencies of energy flux were sig-nificantly higher in pool habitat and at lower flows (Fig. 5),reflecting the higher proportion of available habitat and morefavourable conditions for prey capture (lower velocities, lon-ger reactive distances) in pool habitat (Rosenfeld and Taylor2009). Mean consumption efficiencies in excess of 60% inpool habitat indicate an increased potential for fish to severelydeplete drift flux to downstream channel units at low flows. Alower capture efficiency in nonpool channel units and at higher

flows means that depletion of drift should decline with increas-ing flows or increasing proportion of nonpool habitat at thereach scale.

Bootstrap simulations of available energy and habitat gen-erated large differences in optimal flows among models andchannel structures at the simulated reach scale (100 concate-nated channel units; Fig. 6). There was a significant interactionbetween model (energy flux or PHABSIM using Trinity,Beecher, or Ptolemy habitat suitability curves) and channelstructure (10%–90% pool), such that PHABSIM underesti-mated optimal flows in high-percent pool reaches and overes-timated optimal flows at low-percent pool (Table 2). Thedivergence between WUA and available energy was lowestusing the Ptolemy habitat suitability curves and highest for the

Fig. 4. Flow-related error in available habitat (weighted useable area) relative to available energy flux for the three different sets of habitatsuitability curves (Trinity, Beecher, and Ptolemy), with each row of panels representing a different habitat type. Grey lines represent 90%confidence intervals on the predicted distribution. The horizontal lines represent a relative error of zero (no difference in bioenergetic andWUA predictions).

0 0.1 0.2 0 0.1 0.2

-0.8

-0.4

0

0.4

0.8

-0.8

-0.4

0

0.4

0.8

Glides

Pools

Runs

Riffles

-0.8

-0.4

0

0.4

0.8

0 0.1 0.2

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UA

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Discharge (m3·s-1)

-0.8

-0.8

0.0

0.0

0.0

0.0

0.0 0.0 0.0

-0.8

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Trinity curves (Table 2; Fig. 6). With the exception ofPHABSIM output generated with the Trinity habitat suitabilitymodel, the WUA–discharge curves were generally much flat-ter than the energy flux – discharge curves, with poorly de-veloped optima. Differences in initial slopes among curveswere generally far more pronounced than differences in op-tima, particularly for higher-percent pool reaches, leading to a20%–60% overestimation of available habitat at low flow inreaches with more than 40% pool habitat (Fig. 7). Even formodelling using the Beecher habitat suitability curves, whichwere based on the most rigorous habitat use data for juvenilecoho, PHABSIM output overestimated available habitat at lowflow by 30% in the reach-scale simulation with 40% pool(Fig. 7), causing a much shallower decline in the WUA–discharge curve at low flow (Fig. 6) and contributing to theoverall flatter nature of the WUA–discharge curves relative totrends in available energy flux. The Ptolemy curves based ondata from a wider range of streams performed somewhatbetter, but still showed substantial divergence from predictionsbased on energy flux. Note that variation in bootstrappedsimulations at the reach scale (100 concatenated channel units)in Fig. 7 is lower than illustrated in Fig. 4 because the numberof channel units of each habitat type in simulations generallyexceeds the number of individual replicates in each habitatclass (n � 4), as illustrated in Fig. 4.

Mean inferred focal velocities (velocity at 40% of totaldepth above the stream bed) in occupied cells at optimal flowswere relatively low, averaging 7.1 � 2 cm·s–1 across all 16channel units (maximum mean velocity of 10 cm·s–1 in thechannel unit with the highest predicted inferred focal veloci-ties). Inferred focal velocities in occupied cells also showed noincrease with discharge (slope � –0.006, F[1,142] � 5.1,p � 0.025), indicating that modelled swimming costs did notincrease substantially at higher flows, so that gross energyintake from the foraging model provided a good index ofavailable energy.

DiscussionPHABSIM is the single most widely used habitat availabil-

ity model for detailed instream flow assessments (Tharme2003). Our analysis indicates that standard applications ofPHABSIM systematically underestimate the effects of declin-ing discharge on pool-rearing juvenile coho in small streamsby inflating habitat quality at low flows. Comparison of trendsin available habitat and available energy flux points to adivergence in predicted response to flow generated by model-ing only changes in available physical habitat, while ignoringthe effects of discharge on the flux of invertebrate drift toavailable habitat, which is flow sensitive. This error relative tobioenergetic predictions is of greatest concern at very lowflows, when delivery of energy to available habitat declinesmost steeply and trends in available habitat and availableenergy flux diverge most strongly. The consequence is arelative flattening of WUA–discharge curves (elevation of thedescending limb at low flow), supporting a management in-terpretation that minimizes the negative consequences of waterdiversion from small streams.

Habitat models that predict flow-related changes in produc-tive capacity usually link a physical (hydraulic) model thatpredicts changes in velocity and depth at different flows to abiological model that predicts the biological response to hab-itat change. The biological model in PHABSIM is representedby habitat suitability curves, which predict how relative hab-itat quality (scaling from 0 to 1) changes with velocity or depthand is used by PHABSIM to generate estimates of availablehabitat. Physical habitat models that predict changes in hy-draulic conditions with flow are now fairly well developed andare generally no longer the primary factor limiting reliabilityof model predictions. Arguably, it is uncertainty in the bio-logical response to habitat (i.e., habitat suitability curves) thatgenerates the greatest uncertainty in model output. Advancesin our ability to describe the biological response to flow havelagged behind our ability to model flow-related changes inhabitat (Anderson et al. 2006; Souchon et al. 2008), andhabitat suitability curves remain the most common approachfor modelling how velocity and depth affect habitat quality.Habitat suitability curves for fish are ultimately based onfrequency-of-use data (e.g., density), where habitat is charac-terized in terms of simple physical attributes like velocity,depth, and substrate, but the degree to which frequency-of-usedata accurately reflect habitat quality needs to be carefullyscrutinized (Garshelis 2000; Rosenfeld 2003; Railsback et al.2003).

Density may be a highly misleading indicator of habitatquality (e.g., Van Horne 1983; Railsback et al. 2003; Lan-caster and Downes 2010), particularly for territorial specieswhere dominants can displace subordinates to low-qualityhabitats at high densities. Juvenile coho form strong domi-nance hierarchies, and Beecher et al. (2010) attributed thesevere underestimation of optimal flows observed in theirPHABSIM modelling to dominance hierarchies that result inhigher fish density in lower quality, slow-velocity microhabi-tats (Beecher et al. 2010). In this case, territoriality woulddisplace subordinates to poorer, low-velocity habitats, effec-tively shifting the peak of the habitat suitability curves to alower optimal velocity than would be indicated by growth orproduction rates (Nielsen 1992; Rosenfeld et al. 2005). This

Fig. 5. Average proportion of total energy in the drift that isavailable to drift-feeding fish as a function of discharge and habitattype (pool, circles; glide, diamonds; run, squares; riffle, triangles).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.05 0.1 0.15 0.2

Con

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n ef

ficen

cy o

f drif

tflu

x

Discharge(m3·s-1)

0.100.0

0.00 0.20

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suggests that a habitat suitability curve that more preciselyreflects the fitness consequences of habitat use (i.e., in terms offish growth or production) might produce a more accuratemeasure of available habitat.

Habitat suitability curves that more accurately reflect therelative fitness benefits of microhabitat selection could bebased on measured growth rates of dominant fish (e.g., Nielsen1992; Rosenfeld et al. 2005) or modelled growth using bioen-ergetics under different velocity–depth combinations (Brandtet al. 1992; Garshelis 2000; Jowett et al. 2008). Jowett et al.(2008) demonstrated that bioenergetic- and observational-based habitat suitability curves for adult brown trout are sim-ilar and produce equivalent WUA predictions. In the case ofjuvenile coho, bioenergtic-based habitat suitability curves ap-plied within the current PHABSIM framework might resolvethe discrepancy we observe between PHABSIM estimates ofWUA and predicted changes in energy flux. Bioenergetic-

based habitat suitability curves, while currently unavailablefor most species, are an avenue worth exploring, althoughtheir application requires further validation (i.e., in terms ofhow well they predict flow-related changes in fish growth orabundance).

Ultimately, it is the discrepancy between how flow affectsavailable habitat versus the flux of energy to available habitatthat causes the relative error we observed in PHABSIM mod-elling. For PHABSIM to generate accurate predictions re-quires that productivity of available habitat is either flowinsensitive or follows similar trends to those of availablehabitat. This was not the case for drift-foraging coho, becausetheir preferred pool habitat tends to be discrete from thehabitat that generates their drifting prey (upstream riffles andruns), which shows a more severe response to declining flows.In contrast, there may be less of a mismatch between flows thatoptimize energy flux and available habitat for fish that occupy

Fig. 6. Trends in available energy (a) or available habitat (as weighted useable area) modelled using Trinity (b), Beecher (c), andPtolemy (d) habitat suitability curves for different channel structures (pool habitat ranging from 10% to 90%).

0 0.1 0.2

0

2000

4000

6000

8000

10000

12000

Ava

ilabl

e e

nerg

y (J·

h-1)

90% 70%

40% 10%

Discharge(m3·s-1)

0

100

200

300

400

500

0 0.1 0.2

WU

A (r

each

) WU

A (r

each

)

140

100

60

20

0

40

80

120

800

600

400

200

00.00.0

(d)

)b()a(

(c)

Table 2. Modeled discharges (Qopt) that maximize available energy versus WUA for different habitat suitability curves across a range ofchannel structures (10%–90% pool habitat).

Qopt energy(m3·s–1)

Qopt Trinity(m3·s–1)

�Qopt Trinity(%)

Qopt Beecher(m3·s–1)

�Qopt Beecher(%)

Qopt Ptolemy(m3·s–1)

�Qopt Ptolemy(%)

10% pool 0.149 (0.02) 0.175 (0.02) �17 (20) 0.200 (0.00) �34 (15) 0.168 (0.03) �13 (20)40% pool 0.089 (0.02) 0.109 (0.09) �22 (96) 0.170 (0.06) �91 (64) 0.111 (0.02) �25 (22)70% pool 0.088 (0.01) 0.020 (0.01) –77 (13) 0.06 (0.00) –32 (13) 0.097 (0.01) �10 (13)90% pool 0.091 (0.01) 0.020 (0.00) –78 (11) 0.06 (0.00) –33 (11) 0.083 (0.01) –9 (15)

Note: �Qopt represents the percent deviation of the optimal flow calculated using WUA from the optimal flow estimated using available energy.

Rosenfeld and Ptolemy 1929

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higher velocity habitats, because their occurrence more closelymatches that of their drifting or benthic prey. This would leadto smaller discrepancies between bioenergetic predictions andthe habitat suitability approach for riffle specialists (e.g.,Danehey et al. 1998; Orth and Maughan 1982) or life stagesthat use higher velocities. For example, researchers in NewZealand have shown that WUA is a good correlate of adulttrout abundance (Jowett 1992; presumably, the preference ofadult trout for moderate velocities in the range of 40–50 cm·s–1

(Hayes and Jowett 1994) causes habitat availability to peak atdischarges where invertebrate drift flux remains high, i.e.,without a significant mismatch between trends in availablehabitat and available energy flux. Ultimately, it is the produc-tive capacity of available habitat that drives fish abundance,

rather than gross habitat availability (Shirvell 1986), althoughthe two are often tightly correlated. Understanding when pro-ductive capacity and available habitat are decoupled is there-fore essential to identifying when applications of PHABSIMmodels are most likely to be biased.

Throughout this paper we compare predictions from PHABSIMto energy flux estimated using the drift-foraging model, which wetreat as the more accurate benchmark of changes in productivecapacity, since this model accounts for changes in both availablehabitat and prey availability. However, our drift-foraging modelincludes several simplifying assumptions. First, we assumedthat base drift concentration (numbers and biomass per cubicmetre) was flow-invariant. There is some limited evidence thatinvertebrate drift concentrations decline at lower flows (e.g.,

Fig. 7. Error in modelled available habitat (relative to trends in available energy flux) as a function of discharge at the simulated reachscale for different proportions of pool habitat (10%, 40%, 70%, and 90% pool). Grey lines represent 90% confidence limits onbootstrapped distributions. The horizontal lines represent a relative error of zero (no difference in bioenergetic and weighted useable area(WUA) predictions).

-0.8

-0.4

0

0.4

0.8

0 0.1 0.2 0 0.1 0.2 0 0.1 0.2

90% pool

70% pool

40% pool

10% pool

Bia

s in

WU

A c

urve

s (r

each

sca

le)

Discharge (m3·s-1)

Trinity Beecher Ptolemy

0.00.0 0.0

-0.8

-0.4

0

0.4

0.8-0.8

-0.4

0

0.4

0.8-0.8

-0.4

0

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0.8

-0.8

-0.8

-0.8

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1930 Can. J. Fish. Aquat. Sci. Vol. 69, 2012

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Steingrímsson and Grant 1999; Sotiropoulos et al. 2006), inwhich case the drop in energy flux at very low dischargewould be even greater than our model suggests. Second, weused a drift-foraging bioenergetic model that was calibrated tojuvenile cutthroat trout rather than coho salmon. However,juvenile trout and coho salmon have very similar prey captureefficiencies (Piccolo et al. 2008), and both species have astrong preference for pool habitat that supports their highestgrowth and biomass (Nielsen 1992; Rosenfeld et al. 2000;Rosenfeld and Boss 2001). Since coho are reported to be evenmore dependent on pools than cutthroat trout (e.g., Glova1986), and it is flow effects on available energy in pool habitatthat drive the overall trends observed in our simulations, ourmodel should realistically represent the response of juvenilecoho to changes in flow. Nevertheless, this study remains acomparison between models, and the definitive test of anymodel is to compare predictions to measured growth andproduction of juvenile salmonids exposed to contrasting flowregimes. Given the widespread reliance on instream flow mod-els with somewhat questionable assumptions, experimentalapproaches to validate and refine both flow and bioenergeticmodels in realistic settings are badly needed (Poff et al. 2003;Anderson et al. 2006; Souchon et al. 2008).

Ecologists have long know that fish are limited by bothhabitat and food resources (Chapman 1966; Mundie 1974),and judicious instream flow scientists generally caution thattrends in WUA are only one of several lines of evidence formaking inferences about flow effects on aquatic resources(e.g., Orth 1987; Jowett 1992). Despite this well-intendedguidance, ignoring the potential for discharge-related changesin energy flux to follow a different trajectory than availablehabitat remains a significant shortcoming of PHABSIM appli-cations (in North America in particular) and represents anadditional factor that instream flow mangers must considerwhen interpreting WUA output. The potential for bias may bewidespread for fish that are dependent on drift generated inhigher velocity habitats (e.g., riffles) but occupy very low-velocity habitats (pools), i.e., the ecological guild of low-velocity drift-feeders. Although this bias is simple tounderstand, it is difficult to quantify with confidence, leadingBeecher et al. (2010) to specifically conclude that PHABSIMshould not be used for instream flow assessments for juvenilecoho. In more general application, instream flow managers andscientists need to better identify the suite of circumstanceswhere discrepancies may arise between flow-related changesin available habitat and available energy. Similarly, instreamflow practitioners should be aware that territoriality will resultin bias of suitability curves towards lower flows wheneversubdominants are asymmetrically displaced to lower velocitymicrohabitats (Beecher et al. 2010). Collectively, these biaseswill contribute to underestimating either optimal flows or theconsequences of flow reductions in small streams, as describedabove. This may be of greatest concern for juveniles ofanadromous salmonids, where recruits are less likely to besurplus, than for stream resident fish, where older cohortsare often limited by the availability of deep pool habitat(Armstrong and Nislow 2006).

Although routine North American applications ofPHABSIM generally ignore the effects of flow on prey abun-dance, the need to maintain a prey base for drift-feeding fish is

an explicit rationale for alternative instream flow methodsdesigned to maintain wetted riffle area and associated inver-tebrate production (Annear et al. 2004; Bradford and Heinonen2008). More recent bioenergetic approaches also demonstratethe importance of drift abundance in predicting habitat capac-ity (e.g., Weber 2009; Urabe et al. 2010), and a better under-standing of how flow affects available secondary production(i.e., invertebrate drift) is clearly warranted (Harvey et al.2006). Direct benthic sampling (Jowett and Biggs 2006) aswell as habitat suitability curves and WUA modelling forbenthic invertebrates are widely applied as complementarymethods for determining flows needed to maintain the preybase for drift-feeding fishes in New Zealand (Jowett et al.2008) and were also more commonly considered during theinitial development of the Instream Flow Incremental Meth-odology (IFIM) in North America (Gore et al. 2001). How-ever, the relationship between benthic invertebrate abundanceand predicted changes in WUA in some instances may be poor(e.g., Wills et al. 2006), and confidence in the ability of benthicinvertebrate habitat suitability curves to accurately predictdrift availability is somewhat undermined by a poor correla-tion between benthic invertebrate biomass and drift abundance(e.g., Shearer et al. (2003) found at best weak relationshipsbetween drift and benthic abundance in New Zealand streams).The quality of existing habitat suitability curves for modellingprey abundance is also unclear; the most commonly citedhabitat suitability curve for fish food (invertebrate drift) pro-duction (Waters 1976) is over 35 years old, and the origin ofthe data underlying this relationship is obscure. Althoughexisting benthic invertebrate habitat suitability curves maybroadly identify conditions where drifting prey abundance canbe expected to decline, a lack of research directly linking driftabundance to changes in stream flow remains a major sourceof uncertainty in understanding the effects of flow alterationon juvenile salmonid production.

Given the dependence of many commercially importantor endangered species on adequate flows in small streams(Tennant 1976; Beecher et al. 2010), and the widespread useof habitat availability models like PHABSIM, more rigorousvalidation of model predictions is needed. In addition to earliersystemic concerns related to estimation of available habitatusing PHABSIM (e.g., Mathur et al. 1985), and inadequaterepresentation of confidence intervals (Williams 1996, 2010;Ayllón et al. 2011), we highlight the discrepancy betweenmaximizing available habitat versus available energy flux asan additional source of error in flow assessments. The poten-tially diverse conditions where this could manifest emphasizesthat validation of a habitat availability or PHABSIM approachfor one species or life stage does not provide a proof-of-concept that justifies unvalidated application elsewhere fordifferent species or locales (where validation means demon-strating that predicted changes in WUA reflect observedchanges in fish biomass or production for life stages known tobe habitat limited). Despite its widespread application, ourmodelling results as well as earlier empirical studies (e.g.,Beecher et al. 2010) demonstrate the potential for significantbiases in current applications of PHABSIM and highlight theneed for regional and species-specific empirical validation ofhabitat suitability based models for local flow management.

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AcknowledgementsWe thank several anonymous reviewers for comments that

greatly improved the manuscript.

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