Environmental Biology of Fishes58: 425–438, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

The influence of structural complexity on fish–zooplankton interactions:a study using artificial submerged macrophytes

Jagath Manatunge, Takashi Asaedaa & Tilak PriyadarshanaDepartment of Environmental Sciences & Human Technology, Saitama University, 255, Shimo-okubo, Urawa,Saitama 338 8570, Japan (e-mail: asaeda@env.civil.saitama-u.ac.jp)aTo whom all correspondence should be addressed

Received 18 December 1998 Accepted 4 January 2000

Key words: Daphnia pulex, encounter rates, foraging efficiency,Pseudorasbora parva, visual predation

Synopsis

Aquatic macrophytes produce considerable structural variation within the littoral zone and as a result the vegetationprovides refuge to prey communities by hindering predator foraging activities. The behavior of planktivorousfish Pseudorasbora parva(Cyprinidae) and their zooplankton preyDaphnia pulexwere quantified in a seriesof laboratory experiments with artificial vegetation at densities of 0, 350, 700, 1400, 2100 and 2800 stems m−2.Swimming speeds and foraging rates of the fish were recorded at different prey densities for all stem densities. Theforaging efficiency ofP. parvadecreased significantly with increasing habitat complexity. This decline in feedingefficiency was related to two factors: submerged vegetation impeded swimming behavior and obstructed sight whileforaging. This study separated the effects of swimming speed variation and of visual impairment, both due to stems,that led to reduced prey–predator encounters and examined how the reduction of the visual field volume may bepredicted using a random encounter model.

Introduction

Submerged macrophyte communities are of greatimportance to the dynamics of aquatic ecosystems,affecting both abiotic and biotic processes (for reviewssee Carpenter & Lodge 1986, Jeppesen et al. 1998).In aquatic systems, plant species distribution patternsproduce considerable structural variation within thelittoral zone (Dionne & Folt 1991), and the vegeta-tion can provide refuge for prey communities by hin-dering predator foraging activity (Heck & Thoman1981, Crowder & Cooper 1982, Savino & Stein 1982,1989a,b, Gotceitas & Colgan 1987, 1989, Diehl 1988,Persson 1993, Beklioglu & Moss 1996). Moreover,Crowder & Cooper (1979) suggested that the forag-ing efficiency of vertebrate predators should declinemonotonically with increasing habitat complexity.

Numerous studies (Savino & Stein 1982, 1989a,b,Werner et al. 1983a,b, Gotceitas & Colgan 1987, 1989)investigated the predator–prey interaction betweenlargemouth bass,Micropterus salmoides, and bluegillsunfish,Lepomis macrochirus, to assess the role ofhabitat complexity. These studies reported the choiceof vegetation as safe habitats for young bluegills andalso demonstrated how the predation success decreasedwith structural complexity. In addition, Anderson(1984) examined the foraging behavior of largemouthbass preying on guppies,Lebistes reticulates, andcoenagrionid damselflies under different densities ofvegetation, Dionne & Folt (1991) investigated thefeeding patterns of pumpkinseed sunfish,Lepomisgibbosus, with Sida crystallinaand damselfly larvaeas prey, Heck & Thoman (1981) examined the preda-tion rates of killifish,Fundulus heteroclitus, feeding

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on grass shrimp,Palaemonetes pugio, and Minello &Zimmerman (1983) investigated the interaction of fourestuarine fish predators and juvenile brown shrimp,Penaeus aztecus. The general conclusion of thesestudies was that increased habitat complexity decreasesforaging success of these fish predators.

Conversely, intensity of predation by fish has beensuggested as a mechanism, which causes zooplank-ton to migrate towards vegetated habitats (Cryer &Townsend 1988, Lima & Dill 1990). Experimentalwork by Werner et al. (1983b) showed how preyrespond actively to the presence of predators by reduc-ing activity or by shifting activity to safer habitats.However, if refuges are in short supply, then preymay compete for occupancy of refuges (Holt 1987).Increased selectivity towards such a habitat will depressresource capacity within such habitats, and thereforehabitat selection requires a tradeoff between feedingand predation risk (Brown 1992, 1997).

The relation between foraging efficiency and struc-tural complexity may be specific for any given com-bination of predator and prey (Winfield 1986, Diehl1988). Most of the previous studies focused onmacroinvetebrates or small fish as prey with large pis-civorous fishes as predators. Zooplankton species havean ability to sense and respond to changes such as foodavailability and predation pressure by rapidly alteringtheir patterns of habitat use (Johnsen & Jakobsen 1987,Leibold 1990). Yet, despite the obvious importanceof the refuge potential of macrophytes for zooplank-ton communities, relatively few studies have exam-ined how vegetation structure influences the interactionbetween zooplanktivorous fish and their prey. In onesuch study, the feeding behavior varied among threefish species foraging onDaphnia, under different struc-tural complexities (Winfield 1986). However, the effectof structural complexity on fish foraging behavior wasnot quantitatively analyzed.

Intense grazing by large-bodied zooplankton, espe-cially cladocerans, can lead to large-scale reductionsin phytoplankton crop and consequently increasesin water clarity (Gliwicz & Pijanowska 1989,Jayaweera & Asaeda 1995). One way to increase pop-ulations of large-bodied cladocerans is to reduce pre-dation pressure by providing refugia for vulnerablespecies (Irvine et al. 1990). The littoral zones are struc-turally complex habitats, which provide safe refugefor both invertebrate and vertebrate prey. Predationpressure from piscivores often restricts planktivorousfishes to vegetated refugia (Savino & Stein 1982, 1989,

Werner et al. 1983a,b). A high abundance of small fishgenerally results in a high predation pressure on zoo-plankton in both the pelagic and in the littoral zone(Schriver et al. 1995); however, predation pressure maybe somewhat attenuated because the submerged macro-phytes concomitantly protect zooplankton against pre-dation by fish foraging in the vegetation (Winfield1986, Diehl 1988, Persson 1993).

Crowder & Cooper (1979) postulated that the declinein fish foraging rates with structural complexity is aresult of the predator becoming increasingly obstructedin its movements. However, the feeding performancemay differ among species in relation to their maneu-verability, a trait which will be an asset at highstructural densities (Winfield 1986). In addition, thepresence of vegetation in the foraging environmenttends to increase predator search time and pursuittimes (Crowder & Cooper 1982, Anderson 1984,Cooks & Streams 1984) and also to increase preyevasion (Cooks & Streams 1984). In an earlier exper-imental study, Nelson (1979) showed that the preda-tion rate of pinfish,Lagodan rhomboides, foraging onamphipods is not a simple linear function of struc-tural density. Subsequent studies, which investigatedthe effects of habitat complexity on prey–predatorrelationships, have repeatedly demonstrated a nega-tive nonlinear relationship between predator foragingrate and increasing habitat complexity (Heck & Orth1980, Heck & Thoman 1981, Savino & Stein 1982,1989a,b, 1992, Gotceitas & Colgan 1989). In addition,Gotceitas & Colgan (1987, 1989) showed that there isa threshold level of habitat complexity before forag-ing success is markedly reduced. However, we believeno study has been designed specifically to explore thefeeding behavior of planktivores in relation to such athreshold complexity and to examine the nature of therelationship (e.g. linear vs. nonlinear) between feedingrate and habitat complexity.

Assuming that both planktivores and their preyreside in vegetated areas, we explored how increas-ing plant stem density influences the feeding behav-ior of Pseudorasbora parva(Cyprinidae) preying onDaphnia pulex. We simulated these habitats in lab-oratory experiments and attempted to quantify thebehavioral interactions between these planktivores andtheir prey.Pseudorasbora parvais a widely distributedplanktivore in the littoral zones of freshwater habitatsthroughout Japan, China, and Korea (Okada 1966,Masuda et al. 1988) and can have a marked influ-ence in structuring zooplankton communities (Okuda

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et al. 1996). Also, we tested two hypotheses: (1) therelationship between planktivore feeding rate and habi-tat complexity (i.e. feeding rate vs. stem density) isnonlinear with a threshold level of complexity beyondwhich predator foraging is significantly reduced, and(2) the increased habitat complexity tends to reducesearch velocity and to decrease the prey–predatorencounters by restricting the effective reactive volumeof the planktivore.

Methods

To determine how planktivore feeding efficiency varieswith structural complexity, we analyzed the effects ofa simulated macrophyte stand on the foraging behaviorof Pseudorasbora parvain the laboratory.

Experimental arena

We conducted the experiments in a plexiglass tank56 cm long× 46 cm wide× 48.5 cm deep, filled with120 l of water. Grids of 4 cm× 4 cm were drawn onthe longitudinal sides and the bottom panel of the tank.This tank stood inside a bigger tank mounted on a standunder a light regime of 3500 lux provided by suspendedfluorescent lamps. The inner tank had a water outletpipe for us to remove remaining prey in the tank aftereach foraging experiment. To simulate macrophytes,we used brown cotton rope strands (2 mm diameter,0.7 m long). We attached these rope strands (‘stems’)to a wire mesh at the top and extended to the bottompanel of the inner tank. The bottom panel of the innertank had the same grid structure as that of the wire meshmounted on a stand over the tank. The stems extendedfrom above passed through perforations at the bottompanel of the tank to resemble the same grid pattern ason the mesh above. To quantify how the stem densityinfluenced the foraging activity, we observed the for-aging behavior of the fish at each of five stem densities(350, 700, 1400, 2100 and 2800 stems m−2), as well asthe behavior of the fish without stems (0 stems m−2).Ropes tied 2 cm apart along the length of the tank andthe adjoining line tied with 1 cm displacement from theadjoining line formed the highest stem density (2800stems m−2). Thus, every alternate line along the lengthof the tank had the same line spacing. After completingthe experiments for the highest stem density, for subse-quent experiments, we randomly removed the required

number of stems from the tank arena to achieve eachstem density as indicated above. We generated randomnumbers using a pocket calculator to pick which stemsto be removed from each line.

Tap water was thoroughly aerated at a rateof 16 l min−1, and was dechlorinated by addingTetraChloror dechlorinating solution to the water atproportions suggested by the manufacturer. The tem-perature was maintained at 20–22◦C throughout theexperiment and the turbidity of the water was less than1 NTU.

Predator and prey

We collectedPseudorasbora parvafrom a nearby pondand in the laboratory we kept them in aquariums under12-h light (3500 lux) and 12-h dark conditions. Fish of55–60 mm TL were used throughout the experiments.We fed all fish with commercially available dried foodcontainingDaphnia. The fish were trained to feed onlive zooplankton (density∼ 10 prey l−1) in the exper-imental tank with stems for several weeks before theexperiments. Also, the fish used in each experimentwere acclimatized to the tank environment for 8–24 himmediately before the experiment. The light condi-tions were maintained so as not to interrupt the photope-riod length. The experiments were carried out duringthe light period.

Daphnia pulex, the dominant zooplankton in thepond, was used as the prey. They were grown in tanksas a monoculture in the same laboratory and were fedwith phytoplankton grown in tanks. The temperature ofthe zooplankton tank was maintained at 25◦C through-out and the food was replenished as and when required.Prey of length 0.8–1.0 mm for the experiment were col-lected by serial sieving (using 0.8–1.0 mm sieves) andwere subsequently transferred to a small glass con-tainer (prey stock). Three representative sub-samples(approximately 8–10 ml) were taken to determine theprey density in the prey stock. We only used a givenprey stock for a few hours due to high mortality ofD. pulex, possibly as a result of overcrowding.

Observations and data collection

To determine how macrophyte density influences theforaging success ofP. parva, we concluded a series of3-min feeding experiments where two fish at a timewere tested for foraging efficiency at different prey

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densities (0.5, 1.0, 2.0, 5.0, 10.0, 25.0 prey l−1 of 0.8–1.0 mm D. pulex). For each stem density, we moni-tored the feeding behavior and capture success of thefish at the experimental prey densities. Prior to eachtrial, the fish were starved for at least 24 h; therefore,we assumed that the fish were active in a structurallycomplex habitat at a maximum hunger level when theexperiment began. We observed the feeding behaviorby counting the number of prey attacked during 3 minthrough monitoring windows on both longitudinal sidesof the tank. Two observers sat next to the tank besidethe longitudinal section with an unobstructed view ofthe entire feeding arena and they videotaped the feedingtrials. Each prey density–stem density combination wasrepeated three times (with a new pair of fish for eachexperiment) with two observers monitoring the two fishseparately.

To avoid prey reduction during the foraging exper-iment (especially for 0.5, 1, and 2 prey l−1), we addednew prey samples in concentrations roughly equal tothe number of prey consumed. Before each experi-ment, we made two trials to observe the typical preyreduction rates during small time intervals (25–30 sec);thus we were able to prepare accurate amounts ofsupplemental prey.

The tank was emptied after each experiment (whichlasted for 3 min) and the rope strands were thoroughlywashed with a water sprinkler to remove remainingzooplankton in the tank. The tank was refilled and a newpair of fish was introduced to acclimatize themselves,for at least 8–24 h before the next experiment; thisprocedure was repeated for all the prey–stem densitycombinations.

Swimming patternsTwo Sonyr digital video cameras were used to recordthe swimming and the feeding behavior of fish. Wemounted one camera on a tripod and operated it manu-ally from the side of the tank to follow one fish through-out. The other camera was mounted above the tank tocapture the full plan view of the tank.

The recorded videotapes were played back on a com-puter interfaced with a Sonyr Digital Video captureboard. Sonyr DVBK-W2000 software was used tocapture and analyze the picture frames. Selected por-tions of fish swimming activity (from both top and sideviews) were captured and were saved as video imageswhile watching the tape on the computer screen. Simul-taneous playback of both the top and side views enabledus to analyze the fish swimming movements three-dimensionally. Two sequential picture frames, either

from the top view or from the side view, were thenopened on the computer screen and the distance thata fish moved during that time interval was determinedusing the grids on the tank. The time interval betweenthe two picture frames was recorded automatically inthe picture frame at a resolution of 1/30 sec. The dis-tance fish swam divided by the time interval, both deter-mined by analyzing the two picture frames, producedan approximation for the swimming speed of the fish.At least 30 pairs of picture frames from each of threeforaging experiments (which lasted for 3 min each) foreach prey–stem density combination were analyzed tocalculate the mean swimming speed. In addition, thefish swimming speeds were observed without prey inthe environment.

Estimating the effects of structural complexity

Fish swimming speed is a function of prey densityand structural complexity (Figure 1). And swimmingbehavior is related to hunger level of the forager. Toavoid the effects of satiation on foraging patterns,we carried out the experiments and analyses only forthe first 3 min after exposing the starved fish to theprey environment. During this period fish showed nosigns of satiation. As well as influencing swimmingbehavior, these three parameters also influence the fishforaging rate.

The decreased fish swimming speed due to stemsreduce the prey encounter rates and hence lead to areduction in foraging efficiency. The effective preyencounters decrease further as a result of reducedvisual field volume. This decrease is assumed to bedue to the physical obstruction posed within the visualfield of the fish, which also shields part of the visualvolume. Fish will not avoid stems and turn severaltimes to attack sighted prey, and they capture preymost efficiently when they can pursue prey in straightswimming movements. Therefore, increased numberof stems in the foraging habitat hinder fish movementsand obscure part of the fish’s visual field. Hence, ifa prey item is located in denser vegetation, fish areless likely to initiate an attack. Experimental quan-tification of the obstruction posed by stems is notpossible. Thus, first we used the following modelto evaluate the number of prey encounters as theirexperimental determination was not possible. Theseprey encounter rates were then used to predict theforaging rates ofP. parva, and with the predictedvalues compared with experimentally observed feeding

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Figure 1. Effects of stem density (stems m−2) on swimming patterns ofP. parva(mean TL± 1 SD= 57.48± 1.43 mm, n= 120) for6 prey densities. Vertical bars represent either+1 SD or−1 SD . Standard deviations for swimming speeds with no prey are not shownfor clarity, but the variability is less than the SDs at other prey densities.

rates, we determined the effect of stem presence onfish feeding efficiency. We describe the procedurebelow.

Predicting the prey encounter rateTo quantify the effects of structural complexity onfish foraging efficiency, we formulated a simpleprey encounter model that predicts the feeding rateunder different prey–stem density combinations. Anencounter is an event when a predator and its prey areclose enough so that the predator recognizes the preypresence (Gerritsen & Strickler 1977). If the visual fieldis contained in this recognition volume then,

λ1 = Vvol · N,whereλ1 is the number of prey encounters per unitsearch time, Vvol is the visual field volume and N is theprey density.

A common model for planktivores such asP. parva(personal observation), which employ cruise searchingin foraging, calculates the visual field volume (or thevolume searched) using a cylindrical approximation,where the distance swum is the length of the cylin-der and its radius proportional to the reactive distance

(Confer & Blades 1975, Eggers 1977, Werner et al.1983b, Browman & O’Brien 1992, Aksnes & Giske1993, Letcher et al. 1996). A general form for a plank-tivore’s visual field volume can be obtained, therefore,by the product of the mean (continuous) swimmingspeed and the cross-sectional area of the perceptualfield (Browman & O’Brien 1992):

Vvol = a1 · v,where Vvol is the visual field volume, a1 is the cross-sectional area of the perceptual field, which is a func-tion of the reactive distance and the visual field halfangle (Luecke & O’Brien 1981, Dunbrack & Dill1984), v is the mean swimming velocity correspondingto a particular prey density (N) and stem density. Weused our observed swimming speeds in the laboratorytanks to estimate v forP. parva.

The maximum distance at which a fish reacts to aprey is called the reactive distance for that prey type(Vinyard & O’Brien 1976, Lazzaro 1987, Breck 1993).The reactive distance is a function of light and tur-bidity (which were held constant in our experiments),and prey characteristics, as well as predator character-istics (Lazzaro 1987, Aksnes & Giske 1993). The prey

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used were 0.8–1.0 mm long and therefore, we did notexpect variation in prey size to influence the reactivedistance appreciably. Therefore, we thought reasonableto assume that the reactive distance is a constant for allprey items during our experiments. Therefore, the preyencounter rate of a fish during the experiment is,

λ1 = a1 · v · N.

To account for the reduced prey encounter ratesdue to the obstruction posed by stems in the visualfield volume, we introduced a dimensionless parame-ter, a2(0 < a2 ≤ 1), which is 1 when no stems were inthe foraging environment, and have very small valueswith increasing structural complexity. We assumed thatthe fish pursue prey that are within this reduced visualfield volume and the effective prey encounter rate perunit search time,λ2, becomes,

λ2 = a2 · λ1 = K · v · N,

whereλ1 is the prey encounter rate per unit searchtime in an environment without stems, and K= a1 · a2,therefore, K represents the reduction of the preyencounter rate as a result of reduced visual field volumedue to stems.

The variation of K shows quantitatively how struc-tural complexity affects fish foraging. To determine K,we used the above prey encounter model to predict theforaging rate of the fish (as described below), whichwe compared with the feeding observations from ourlaboratory experiments.

Predicting the foraging rateWe used a stochastic algorithm to determine if the preywere located and eaten as described by O’Brien et al.(1989). First, the likelihood of prey being present inthe effective search volume (i.e. a1 · a2 · v) was deter-mined. Assuming that the fish’s search is a Poisson pro-cess, the probability of one or more prey being in thesearch volume (Gerritsen & Strickler 1977, Peterson &DeAngelis 1992, Breck 1993) is,

P(prey≥ 1) = 1− exp(−λ2),

where λ2 is the effective prey encounter rate asdescribed above.

If this probability is high (i.e. a value very closeto 1), then the chances of fish eliciting an attack arealso high. However, when the probability of prey pres-ence is appreciably less than 1, we were uncertain of

the prey’s presence within the fish’s visual field vol-ume. In this situation a random number was generated,and was compared with the probability of prey pres-ence. If the generated random number was less thanor equal to the probability, then prey presence withinthe visual field volume was assumed (O’Brien et al.1989).

Searching is a continuous process for a cruisesearcher and we assumed the search to continue untilthe fish located a prey. The search time increased by dis-crete time steps of 0.01 sec, and we calculated the preyencounter rate and the probability of encounters, untilthe fish successfully encountered a prey. Once the fishlocates a prey it attacks it. We predicted the success ofan attack using another random number, and comparedit with a 90% capture success (personal observation forP. parva– see below).

If prey are present within the visual field volume,then the fish will initiate an attack. We used the fol-lowing two parameters observed from video analysisto calculate the feeding rates ofP. parva.(1) Attack time+ handling time= 0.42± 0.031 sec

(mean± 95% C.I., n= 50) (if the attack wasunsuccessful, then half this time was taken as theattack time).

(2) Capture success= 90%. Capture success wasnot affected by increasing structural complexity.Starved fish, when exposed to prey, were veryactive and accurate in eliciting an attack, but thiscapture success tended to decrease with satiation.For the three-minute observation period, the cap-ture success remained near 90%.

Calculation of the initial value of KThe initial value of K used in the prey encounter modelwas first estimated for a ‘no stem’ environment. Weobserved the reactive distance ofP. parvausing severalvideo frames recorded for feeding trials. Although wedid not make exact measurements, we observed thatthe reactive distance (R) was approximately 4–5 cm.We followed the cross-sectional area of the perpetualfield [= π(R. sinθ)2] as proposed by Aksnes & Giske(1993). We assumed a reactive distance half angle(θ)

of 60◦ and calculated the K value at this stem den-sity as between 37–58 cm2. We predicted the forag-ing rate (number of prey eaten per 3 min) using theabove-described procedure for different prey densitiesat ‘no stem’ density. We assumed the initial value of Kwas within the above range for all prey densities. Wecompared the predicted foraging rate with the observed

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values for this stem density as a function of prey den-sity with systematic calibration of K until we obtaineda best fit (using linear regression methods).

Final evaluation of KWe assumed the initial value for K for a particular stemdensity and we predicted the number of encounters fordifferent prey densities, while keeping K constant foreach stem density. Subsequently, we predicted the feed-ing rates for other stem densities and we calculated theK values similar to the ‘no stem’ environment by com-paring the predicted feeding rates with the observedrates. The K value that produced the best fit betweenthe observed and the predicted foraging rate at a certainstem density was used as the final value for that stemdensity.

Statistics

Nonparametric ANOVAs (Kruskal–Wallis tests) wereperformed separately at each prey density (includingwhen no prey was in the environment) to analyze theswimming behavior. Multiple comparisons were car-ried out using the Tukey–Kramer test. The relation-ship between the predator foraging rate and structuralcomplexity was investigated using regression analy-sis. The departures from linearity were obtained froma regression ANOVA table that compares deviationsfrom linearity with scatter among replicates (‘withingroups variability’ – see Zar 1999, pp. 345–350). Thenull hypothesis tested was that of a linear relationship.

Table 1. Results of multiple comparisons between mean swimming speeds ofP. parvaacross six stem densitiesfor six different prey densities. The asterisks indicate where the mean swimming speed was significantly different(Tukey–Kramer test, respective significance levels are given) between respective stem densities at each preydensity.

Stem density (stems m−2)

Preydensity(prey l−1) 0

vs.3

5

0vs

.700

0vs

.140

0

0vs

.210

0

0vs

.280

0

350

vs.7

00

350

vs.1

400

350

vs.2

100

350

vs.2

800

700

vs.1

400

700

vs.2

100

700

vs.2

800

1400

vs.2

100

1400

vs.2

800

2100

vs.2

800

0.5 ns1 * 2 ** *** *** ns * *** *** ns * ** ns ns ns1.0 ns ** *** *** *** ns ** *** *** ns ** *** ns ** ns2.0 ns *** *** *** *** ns ** *** *** ns ** *** ns ** ns5.0 ns * *** *** *** ns ** *** *** ns * *** ns ns ns

10.0 ns ** *** *** *** ns ** *** *** ns ns * ns ns ns25.0 ns * ** *** *** ns * ** *** ns ns ns ns ns ns

1ns= mean swimming speeds not significantly different (p> 0.05).2Mean swimming speeds significantly different (p<* 0.05, ** 0.01, *** 0.001).

Results

Swimming patterns

Figure 1 shows the swimming speeds ofP. parvaasa function of stem density at different prey and stemdensities. ANOVAs were performed separately ateach prey density (including when there were noprey in the environment) to analyze the swimmingbehavior. In general, increasing plant density reducedthe mean swimming speeds of the fish (Kruskal–Wallis, p< 0.001). Table 1 shows the results of amultiple comparison post-test (Tukey-Kramer test)performed.

The swimming behavior of the fish was affected byboth stem and prey density variations. The maximumforaging speed (9.39 cm s−1 or approximately 1.6 bodylengths s−1) was observed in an environment devoid ofstems and at the lowest prey density of 0.5 prey l−1.This swimming speed decreased with both increasingstem and prey density (Figure 1). Zero stem densitycorresponded to fish foraging in an open tank withoutstems.

Foraging rates

By increasing the habitat complexity (without increas-ing prey abundance), fish foraging rates significantlydecreased (Kruskal–Wallis test, p< 0.001 for all preydensity treatments; Figure 2). Decreasing the preyabundance reduced the capture rates in all cases.

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Figure 2. Effects of plant density on capture rates (per 3 min) ofP. parvafor different prey densities (prey l−1). Horizontal bars indicatethe mean feeding rates that were not significantly different between stem densities (Tukey–Kramer test, p< 0.05). Vertical bars represent±1 SD.

Figures 2 and 3 show the corresponding foraging pat-terns together with the results of the multiple com-parison tests (Tukey-Kramer test). Using a regressionANOVA table, the relationships between fish forag-ing rates and stem density across each prey densitywere found to deviate significantly from a linear rela-tionship [F= 4.01 (0.5 prey l−1), 12.41 (1.0 prey l−1),28.08 (2.0 prey l−1), 11.29 (5.0 prey l−1), 28.98 (10prey l−1), 15.77 (25 prey l−1); p< 0.001 for all preydensities other than 0.5 prey l−1, for which p< 0.01;d.f . = 4, 30 for all prey densities].

To analyze the effects of prey abundance (Figure 3),capture rates ofP. parva were compared at equalstem densities. Capture rates increased significantlywith increasing prey density (Kruskal–Wallis test, p<0.001). At zero and 350 stems m−2, high prey den-sities (10 and 25 prey l−1) led to high feeding rates,where the mean capture rates were not significantlydifferent from each other (p> 0.05, Figures 3a–f).This suggests that at low structural complexities, thepredator may not experience a significant effect on itsforaging behavior when prey are abundant. Predatorfeeding rates were not significantly different at lowprey densities, when the stem density was higher than700 stems m−2. The feeding rates may not have com-pensated the effects posed by the stem density incre-ment although the prey availability slightly increased(e.g. from 0.5 to 1.0 prey l−1 or 1.0 to 2.0 prey l−1).The relationships between prey density and foragingrates differed significantly from a linear relationshipat 0 stems m−2 (F4, 30 = 19.69; p < 0.001 ), 350stems m−2 (F4, 30 = 11.16; p< 0.001), 700 stems m−2

(F4, 30 = 4.20; p < 0.01) and at 1400 stems m−2

(F4, 30 = 3.59; p < 0.05), however, the departuresfrom linearity were not significant at 2100 stems m−2

and 2800 stems m−2 (F4, 30 = 1.29 and 1.01, respec-tively; p> 0.05).

The differences observed above were, therefore,great enough to suggest that the foraging rate ofthe planktivore is significantly affected by structuralcomplexity of the environment. Moreover, the fact wasevident that the foraging ability ofP. parvavaried non-linearly with increasing stem density.

Effect on prey encounter patterns

The foraging rates predicted using the prey encountermodel were compared with observed values (Figure 3).Figure 4 shows the calibrated values for K as a functionof stem density.

The prey encounter rate decreased sharply whenthe stem density increased from 350 to 700 stems m−2

(Figure 4). Thereafter, the decrease was moderate up to2100 stems m−2. A stem density up to 350 stems m−2

did not affect the foraging activity significantly. More-over, the reduction in the foraging activity when thestems increased from 2100 to 2800 stems m−2 was alsonot very significant. Therefore, we can conclude thata clear threshold existed at which the foraging activ-ity decreased markedly when the stem density wasbetween 350 and 700 stems m−2.

Discussion

Submerged vegetation obstructs sight and impedesswimming behavior of foraging fish. Our study showed

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Figure 3. The feeding rate (per 3 min) ofP. parvaas a function of prey density for 6 stem densities, as observed in the experiments andas predicted by the prey encounter model. a – nostems; b – 350 stems m−2; c – 700 stems m−2; d – 1400 stems m−2; e – 2100 stems m−2;f – 2800 stems m−2. Vertical bars represent±1 SD for observed feeding rates. Points marked with the same letter indicate mean feedingrates that were not significantly different (Tukey–Kramer test, p< 0.05).

that the structural complexity created by aquatic veg-etation significantly reduced the foraging efficiency ofP. parva feeding onDaphnia pulex. Also, this studyseparated the effects of swimming speed variation andreduced prey encounters due to visual impairment, bothdue to stem presence, and examined how the reduced

visual field volume may be predicted using a randomencounter model.

According to its definition, the calibrated value forK (the parameter introduced to represent the reduc-tion in the visual field volume) was highest when nostems were in the foraging environment (Figure 4) and

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Figure 4. The effects of stem density (other than reduced swim-ming speeds) on feeding behavior ofP. parvathat led to decreasedprey encounters as quantified by the parameter K (see text for thedescription of the parameter). A smooth curve has been fitted toshow the trend of how the K values decrease with increasing stemdensity. Percentage reductions of encounter rates with increas-ing stem density are also shown. The reductions were calculatedassuming the predator was capable of feeding at the highest ratein an environment without stems.

gradually decreased with increase in stem density, adecrease that can be related to the reduction in the effec-tive prey encounter rates. The effective prey encounterrate can be described as those encounters that elicitan attack from the fish. The reduced visual field vol-ume in an environment with stems, compared with anopen water habitat, is due to physically concealing orshielding the frontal view. Second, we presumed thatthe fish avoid attacking prey in part of the visual fieldvolume due to the obstruction to its movement by thestems. These two factors restrict the ‘effective’ visualfield volume of the fish. Figure 4 shows the resultantpercentage reduction in the effective prey encounterrates. The number of prey encounters in an environ-ment without stems was taken as 100% to calculatethe reductions in the encounter rate when the fish wasforaging within the stem environment.

Increasing stem density in the foraging environmentled to a sharp decrease in the effective visual fieldvolume and thus the effective prey encounter rates.The reduced swimming activity was introduced in theencounter model as observed and, therefore, furtherreduction in prey encounters can be regarded as aresult of a decrease in the visual field volume due tostems in the foraging environment. The fish encoun-tered fewer prey at low prey densities. The structuralcomplexity, which acts as a visual barrier and a swim-ming obstruction, further reduced the prey–predatorencounters, which may have led to the fish search timeto increase sharply at lower prey densities (e.g. up to

2.0 prey l−1) associated with high stem densities as sug-gested by Figures 3d, e, f. Anderson (1984) observedsuch an increment in search time while foraging withina structurally complex environment.

Our results are similar to those of previous stud-ies (Heck & Thoman 1981, Savino & Stein 1982,1989a, Minello & Zimmerman 1983, Winfield 1986,Diehl 1988, Gotceitas & Colgan 1989). In addition,our results quantitatively estimated the effects of sub-merged vegetation in planktivore foraging efficiency.Moreover, this study extended the previously observednonlinear relationship between habitat complexity andthe foraging success of a predator (see Gotceitas &Colgan 1989 for a complete description) to includeplanktivore behavior. Gotceitas & Colgan (1989), usinglargemouth bass (25–35 cm SL) as the predator andbluegill sunfish (3–6 cm SL) as prey, found a thresh-old value of plant stem density at 276 stems m−2

that reduced predator foraging success significantly.Their results corresponded well with their previouswork (Gotceitas & Colgan 1987) and observations ofSavino & Stein (1982). Our observation of a thresholdvalue between 350–700 stems m−2 proposes a valuefor planktivores, when the characteristics of prey andpredator used in this study are taken into consideration.The relationship between environmental structure andfeeding behavior is a function of absolute and relativesizes of prey and predator. Smaller zooplanktivores areless impaired by stems than large piscivorous fishes,partly attributable to differences in body size and shape,and differences in maneuverability (Winfield 1986).Therefore, our results can only be compared with pre-vious studies that explored the feeding behavior of pis-civores, with caution.

The influence of stem density onP. parvacapturerates at constant prey densities could have been dueeither to changes in the swimming speed, the decreasedprey encounter rates through the physical interferenceof the structure with the reactive volume, or both.The feeding efficiency analysis (expressed as prey cap-tures in 3 min) indicated that the observed capture rateswere not only an outcome of the variation of swim-ming speed. The decrease in prey capture rates withstem density (Figure 2) was greater than the decreasein swimming speed due to stems (Figure 1) at eachprey density. Therefore, the reduced prey capture rateswith increasing stem presence cannot be a sole func-tion of swimming reductions alone. We can reasonthat the further reduced prey capture rate, which wasnot explained by the reduced swimming speed, was

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due to the reduced visual field volume as a resultof the presence of macrophytes, as was described inour foraging analysis. The hypothesis that the preyencounter rates decreased because of the stems restrict-ing the effective search volume was explained usingthe introduced parameter K in our analysis. The factthat the K value decreased with structural complexityshows that the effective search volume was increas-ingly restricted with increasing stem presence. Hencethe stem density influenced the ability ofP. parvatoreact to prey, and restricted the effective prey encoun-ters. Prey abundance also influenced the capture rates.The fish adjusted their swimming speeds accordingto the prey availability (Figure 1), which affected theencounter rates at different prey densities and hencethe capture probability. However, in this analysis the Kvalue was always related to the stem density by assum-ing a constant value for each stem density.

The prey sizes we used in the experiments were0.8–1.0 mm, that is a maximum of a 20–25% differ-ence in prey sizes. We also assumed that the reactivedistance of the fish was independent of prey abundanceand stem density. A sizeable variation in prey abun-dance and stem density can result in a slight differencein the reactive distance of the fish to these prey andhence the encounter rates. The distribution ofDaphniawas not affected by stem presence during the 3 min ofthe experiment. Their distribution appeared to be ran-dom and clumping of prey was not observed during theexperiment.

We stressed that visual obstruction resulted inincreased search time due to the sharp decline in effec-tive prey encounters with increasing stem density. Theactivity of largemouth bass preying upon bluegill sun-fishes declines with increasing stem density of the arti-ficial vegetation due to a precipitous decrease in visualcontact with prey (Savino & Stein 1982). Both searchand handling times of largemouth bass preying on dam-selflies are significantly higher in dense stands of arti-ficial Elodeathan in sparse stands (Anderson 1984).Anderson concluded that variation in structure pro-duces marked changes in almost all aspects of the forag-ing process, including search and handling times, andrates of predator movement while searching and han-dling prey. Cooks & Streams (1984) observed longersearch times in the presence of vegetation for twosunfishes foraging onNotonectaspecies. Our studyshowed that the reduced zooplankton vulnerability topredation in vegetation was due to the impaired reactivefield making part of the visual field volume ineffective,

and resulting in fewer prey encounters and longersearch times. Stems did not obstruct the immediatevicinity of the predator and when the prey were avail-able in very high densities, the foraging rate was notaffected significantly. However, the level of protectionafforded by vegetation to major prey types dependedon prey size relative to vegetation density and overallinteraction strength between predator and prey.

Structure had little influence on eventual prey cap-ture success. At least 90% of all capture attempts by fishon Daphnia were successful at each structural level.The capture success of largemouth bass when feed-ing on damselflies and guppies is unaffected by differ-ent structural densities (Anderson 1984). The physicalstructure of plants affects the ability of pumpkinseedsunfish to locate prey, but does not affect their ability toattack prey successfully once located (Dionne & Folt1991).

From theoretical developments and laboratoryexperiments, we have a better picture of the behav-ioral processes by which planktivores adapt their useof space in the presence of structural complexity. How-ever, we cannot yet predict clearly their behavior innatural conditions. The natural macrophyte habitatcould be more complex than we simulated. Dionne &Folt (1991) showed that plant morphology also affectsfish foraging behavior. They found marked variationbetween the capture rates of pumpkinseed sunfish for-aging among leafy stems (Potamogeton amplifolius)and cylindrical stems (Scirpus validus). Plants withmore complex, finely divided leaves provide more pro-tection than simple unbranched stems (Heck & Orth1980) specifically for small prey types. Diehl (1988)compared the effects of simultaneous changes in bothdensity and form of two artificial plant types (resem-bling PotamogetonandChara) and found distinct dif-ferences in fish foraging behavior in two differenthabitat types. However, conducting experiments in thelaboratory to follow the general trend in planktivore for-aging behavior in structurally complex habitats mightbe feasible. To relate the laboratory-simulated stemsto field conditions, Savino & Stein (1982) showed thatnatural vegetation provides more cover than artificialvegetation at similar densities. They predicted that theeffect on predator behavior at 250 artificial stems m−2

(0.4 mm diameter and 0.5 m long) is approximatelyequal to 130 stems m−2 of Potamogeton natans. Thestem density of this study provided less cover than thestudy of Savino & Stein (1982) because we used stemdiameters smaller than those used by them.

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Predators respond differently to physical structure

Minello & Zimmerman (1983) observed that vegeta-tive structure reduces predation rates of pinfish butdoes not affect predation rates of red drum, bothforaging on juvenile brown shrimp. In laboratoryexperiments with three littoral fish species feedingon Daphnia magna, Winfield (1986) observed thatin the absence of environment structure the roach isthe most efficient predator, but stem presence raisedperch to this position. For rudd, he observed a uni-modal relationship between stem density and zoo-plankton capture rates, where the prey captures werehighest at intermediate stem densities. The varia-tion in foraging between these species was partlyattributed to differences in fish morphology, such asbody form, which favors the utilization of the complexenvironment.

By performing several enclosure studies, Schriveret al. (1995) showed that fish predation impacts thezooplankton community less in the most structuredenvironments. However, they observed that the refugepotential of macrophytes for the zooplankton commu-nity decreases markedly with increasing fish densityinhabiting the plant stand. Furthermore, they concludedthat macrophyte abundance might have a positiveimpact on the zooplankton leading to a lower phy-toplankton biovolume and higher water transparency.Larger Cladocera are most likely to be effective ingrazing algal populations, particularlySimocephalusand Sida among the plant beds andDaphnia andCeriodaphniain the open water (Peters 1984, Irvineet al. 1989). However, these species are particularlyvulnerable to fish predation due the their slow move-ments and low evasive capability (Drenner et al. 1978,Manatunge & Asaeda 1999).

Size selective predation is a major factor in thestructuring of zooplankton populations (see Lazzaro1987 for a review) resulting in the domination of smallsized zooplankton by severe fish predation. Under-standing the mechanisms by which vulnerability ofzooplankton to fish predation is reduced has been themain focus of biomanipulation during the past threedecades.

Aquatic macrophytes can contribute to an increase infish abundance (Heck & Thoman 1981, Killigore et al.1989), as well as in invertebrate abundance (Savinoet al. 1992). Once plant communities that can occupythe entire water column reach their maximum densityin summer, the fish movements and foraging efficiency

decrease considerably. In such situations, intermediatedensities of submerged vegetation contain more fishspecies and greater numbers than areas having densegrowth (Killigore et al. 1989). We can expect to findincreasing numbers of predators attracted to the preyinhabiting vegetated areas, as long as the amount ofvegetation is insufficient to reduce foraging efficiencyto less than it would be in other habitats (Heck & Orth1980). Savino et al. (1992) examined in detail the con-flicting consequences to a predator of increasing vege-tation (i.e. an increase in the prey density and a decreasein the capture rate). An increased predator populationwithin the vegetation influences the habitat use patternsof large species of zooplankton (Raess & Maly 1986).Also, if the algivorous fish population within the veg-etation increases, intense algivory may alter the qual-ity of habitat available for the zooplankton community(Gelwick & Matthew 1992).

The results of this study clearly showed that theforaging opportunities ofP. parvawithin the vegeta-tion are reduced relative to vegetation-free habitats.Pseudorasbora parva, being a small planktivore, hasto search for its food while not becoming food for itspredators and should seek refuge from predation. If thevegetation structure provides a safe habitat,P. parvamust trade off energy gain against the risk of preda-tion (Werner et al. 1983b, Mittelbach 1984, Lima &Dill 1990). When the intensity of predation changes,such planktivores may benefit by facultatively chang-ing their habitat selection behaviour to optimize thetrade-off between predation risk and the other effects,such as food and temperature (Leibold 1990). Also,planktivores can use vigilance behaviors to alter therisk of predation while engaged in a particular activity(Lima & Dill 1990).

Costs of using macrophyte habitats as refuges forzooplankton may exist as well. Zooplankton tendto aggregate in patches, which have a high abun-dance of food relative to other patches (Holt 1987).While foraging in a single patch a zooplankton should,through exploitation, decrease resource abundances,thus making that patch a less profitable place to for-age for itself and others (Brown 1997). If there isa severe competition for resources (Holt 1987), zoo-plankton should abandon such habitats when the har-vest rate no longer compensates for the metabolic,predation, and missed opportunity costs of forag-ing (Brown 1992). Therefore, habitat choice for zoo-plankton requires a tradeoff between feeding rate andpredation risk. Predation by planktivores may also

437

be mitigated by swarming of zooplankton – denseconcentrations of similar prey reducing vulnerabilitythrough a ‘confusion effect’ (Milinksi 1984, Johnsen &Jakobsen 1987).

The effect of stem density on planktivorous forag-ing behavior has been inadequately addressed so far(J. Savino personal communication), yet structurallycomplex aquatic habitats have potentially broad impli-cations for future studies of fish foraging in suchecosystems. Although new information has appearedin recent years about the impacts of vegetation onfish–zooplankton interactions (Schriver et al. 1995,Jeppesen et al. 1998), understanding these interactionsremains poor. Most previous studies were carried outwith only one predator, but a great need exists for mul-tispecies experiments. A better insight into the inter-action between fish–zooplankton–macrophytes alsorequires a more thorough knowledge of the feed-ing behavior of planktivores in varying environmentalconditions.

Acknowledgements

Howard I. Browman and Jacqueline F. Savino com-prehensively reviewed earlier drafts of this work. Themanuscript was substantially improved from com-ments provided by anonymous reviewers. We thankthem all. The research was financially supported bygrants from the Ministry of Education, Japan (ResearchGrant-in-Aid), Foundation of River and WatershedManagement and Maeda Engineering Foundation toT.A. The study was carried out while J.M. was receiv-ing a postgraduate scholarship from the Ministry ofEducation, Science, Sports and Culture (Monbusho),Japan.

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