Journal of Fish Biology (2000) 57, 291–302doi:10.1006/jfbi.2000.1323, available online at http://www.idealibrary.com on

Factors related to gizzard shad and threadfin shadoccurrence and abundance in Florida lakes

M. S. A*, M. V. H D. E. C, J

Department of Fisheries and Aquatic Sciences, The University of Florida,7922 NW 71st Street, Gainesville, FL 32653, U.S.A.

(Received 28 April 1999, Accepted 16 March 2000)

Gizzard shad Dorosoma cepedianum were collected in 23 and threadfin shad D. petenense werecollected in 22 of the 60 Florida lakes sampled. Logistic regression equations were 94%effective for predicting gizzard shad occurrence from chlorophyll and lake surface area, and84% effective for predicting threadfin shad occurrence from lake surface area and lake volumeinhabited (PVI). Occurrence of both shad species was related positively to lake size. In lakeswhere gizzard shad or threadfin shad were collected, shad density and biomass of both shadspecies were related positively to chlorophyll. Gizzard shad populations were generallyvulnerable to predation in lakes, with the per cent of gizzard shad �200 mm LT valuesexceeding 60% with few exceptions. Effects of gizzard shad and threadfin shad on fishcommunity dynamics may be confined to relatively large (>100 ha) and fertile (chlorophyll>20–30 �g l�1) Florida lakes. � 2000 The Fisheries Society of the British Isles

Key words: gizzard shad; threadfin shad; trophic state; aquatic macrophytes.

*Author to whom correspondence should be addressed. Tel.: +1 352 392 9617; fax: +1 352 846 1088;email: msal@gnv.ifas.ufl.edu

INTRODUCTION

Gizzard shad Dorosoma cepedianum (Lesueur) and threadfin shad D. petenense(Gunther) are important fishes in many lentic systems, frequently contributingthe majority of fish biomass present (Jenkins, 1967; Noble, 1981). Because theyare often highly abundant, gizzard shad and threadfin shad are important preyfishes for predators such as black basses Micropterus spp., temperate bassesMorone spp., and crappies Pomoxis spp. (Noble, 1981; Heidinger, 1983).However, the relative value of gizzard v. threadfin shad to predators differs, withgizzard shad often growing too large as adults (>200 mm total length LT) forconsumption by most sport fishes whereas threadfin shad remain small enoughas adults (<130 mm, LT) to be utilized by most predators (Noble, 1981).Although both shad species serve as prey for adult piscivores, the shads mayinfluence other members of the fish community negatively via interspecificcompetition, particularly during larval or juvenile phases (DeVries et al., 1991;DeVries & Stein, 1992). Thus, gizzard shad and threadfin shad may be pivotal instructuring the aquatic communities where they exist by providing forage foradult piscivores or by influencing recruitment of other fishes via interactionsduring early life (Stein et al., 1995).

However, shad population characteristics and potential interactions with otherfishes vary substantially across systems. Density and biomass of both shad

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species increase with trophic state (Jenkins, 1967; Siler et al., 1986; Bachmannet al., 1996; DiCenzo et al., 1996; Ney, 1996). Thus, the potential for both shadsto provide abundant prey and interact with other fishes during early life probablyincreases with trophic state. DiCenzo et al. (1996) found that gizzard shadpopulations in eutrophic reservoirs had high densities and reduced growth ratescompared with gizzard shad populations in oligo-mesotrophic reservoirs, sug-gesting that density-dependent growth occurred in eutrophic reservoirs.Eutrophic reservoirs contained gizzard shad that were more vulnerable topredation than rapidly growing ones in oligo-mesotrophic reservoirs (DiCenzoet al., 1996). Considering presence of aquatic macrophytes, Bettoli et al. (1993)found that threadfin shad abundance increased about five-fold after aquaticvegetation was removed from one reservoir with grass carp Ctenopharyngodonidella (Valenciennes), but gizzard shad densities were not related to vegetationabundance across the same time span due to erratic recruitment. Clearly,abundance and size structure of both shad species will vary with factors such astrophic state, lake size and vegetation abundance. However, no studies haveevaluated both shad population characteristics (e.g. occurrence, density, sizestructure and vulnerability to predators) across lakes that differ greatly in trophicstate, surface area, and vegetation abundance.

Florida contains over 7700 natural lakes that range in trophic state fromoligotrophic to hypereutrophic, range in size from <1 to 180 000 ha, and rangein coverage of macrophytes from 0 to 100% (Canfield & Hoyer, 1988). Theobjectives of this study were to identify factors related to occurrence, density andbiomass of gizzard shad and threadfin shad in a sample of Florida lakes.

METHODS

Data for this study originated from studies conducted by Bachmann et al. (1996),Hoyer & Canfield (1996a) and Allen et al. (1998). Sixty lakes were selected using astratified sampling design based on trophic status and vegetation abundance. Withineach of the major trophic categories (oligotrophic, mesotrophic, eutrophic, andhypereutrophic based on measured chlorophyll values and the classification scheme ofForsberg & Ryding, 1980), lakes were chosen that had macrophyte coverage rangingfrom <10–>75%.

Each lake was sampled once between June 1986 and June 1990, and all lakes weresampled between May and November. All samples from each lake (i.e. chlorophyll,macrophyte, zooplankton and fish sampling) were collected in the same year, but due tothe logistics of sampling the lakes were not all sampled in the same year. Sampling foraquatic macrophytes was conducted with transect sampling using a Ratheon DE-719fathometer according to the methods of Maceina & Shireman (1980). Vegetation wasexpressed as percentage of lake volume inhabited (PVI), which represented the per cent oftotal lake volume that was occupied by aquatic macrophytes. Water samples werecollected from six stations (three limnetic and three littoral) on one date and threelimnetic samples were collected on two additional dates. Chlorophyll concentration (�gl�1, chlorophyll) was determined as described by Yentsch & Menzel (1963) and Parsons& Strickland (1963).

Zooplankton samples were collected at four to six stations from each lake using aWisconsin net (12 cm mouth diameter, 80 �m mesh) towed vertically through the watercolumn from 0·5 m above the bottom to the surface. Each sample was reduced into a50-ml volumetric flask, and a sub-sample of 5 ml was placed in a counting wheel andexamined under 40� magnification, and the sub-sample was expanded to the totalvolume of the sample. Zooplankton were combined as the total number of cladocerans,

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copepods, nauplii and rotifers per litre from each sample, and samples were averaged toobtain mean total zooplankton density (organisms l�1) for each lake.

Gizzard shad and threadfin shad were collected with 0·08-ha block nets treated withrotenone at each lake (Shireman et al., 1981). Two to 12 block nets were set at each lakedepending on lake size, but the majority of lakes (58) were sampled with six nets.Rotenone was applied at 2 mg l�1, and fish were collected for 3 days after rotenoneapplication. Equal numbers of block nets were set in littoral and limnetic habitats, andthe littoral zone was categorized as either the zone containing vegetation that reached thewater surface around the lake (n=52), or if emergent vegetation was absent (n=8) thelittoral zone was calculated as the zone from the shoreline to the edge of the block net(about 30 m) times the shoreline length. Biomass (kg ha�1) and density (number ha�1)estimates of gizzard shad and threadfin shad were weighted by habitat (littoral andlimnetic area) to obtain whole-lake estimates. All shad from block nets were grouped in40-mm size groups and weighed. Fish �200 mm LT were classified as vulnerable topredation, and fish >200 mm as not vulnerable to predation. The proportion of gizzardshad �200 mm LT was used as an index of vulnerability for each population (DiCenzoet al., 1996).

Catch-per-effort (CPE) in gill nets was used as another measure (in addition to blocknets) of presence and abundance of gizzard shad and threadfin shad. Three to six (usuallythree) experimental gill nets were set once in limnetic areas of each lake. Gill nets were50 by 2·4 m and each had five 10-m panels of different bar mesh sizes (19, 25, 38, 51,76 mm). Gill nets were set for 24 h and catches were standardized per net night(CPE=number net�1 24 h�1). The CPE from all nets were averaged within each lake.

Data analysis was conducted with logistic and multiple regression. Logistic regressionwas used with backward selection (SAS, 1994) to develop models describing presence orabsence of gizzard shad and threadfin shad based on lake surface area, chlorophyll, totalzooplankton density and PVI. Because lake surface area, chlorophyll, total zooplanktondensity, and gizzard shad and threadfin shad biomass and density values spanned ordersof magnitude and variances were proportional to means, these data were transformed totheir base-10 logarithms after adding one to all values. Per cent data (PVI) weretransformed using an arcsine square-root transformation (Zar, 1984). The linear modelused was:

logit(pj)=a+b1(x1)+b2(x2) . . . bi(xi) (1)

where: logit(pj)=the logistic probability of the species j being collected in lake i;a=intercept value; bi=parameter estimate and xi=independent variable for lake i. Theestimate of logit(p) was used to obtain the predicted probability of gizzard shad orthreadfin shad occurrence (p) as:

p=1�elogit(p) (1+elogit(p))�1 (2)

Independent variables were included in the model if the maximum likelihood test (Wald’s�2) was significant (P�0·05), and the backward selection procedure terminated when noother independent variable could improve the overall model significantly (P�0·05). Thevalue of c (SAS, 1994) was used as an index of rank correlation to assess the predictiveability of the model. The value of c indicates the per cent effectiveness of the model forpredicting a binary response (SAS, 1994).

For lakes that contained gizzard shad or threadfin shad, factors were related toabundance of both shad species and size structure of gizzard shad. Multiple linearregression was used with backward selection to describe the density, biomass, andcatch-per-effort in gill nets of each shad species as a function of lake surface area,chlorophyll, total zooplankton density, and PVI. The backward selection proceduresterminated when no other independent variable could improve the model significantly(P�0·05). To evaluate how gizzard shad size structure varied with trophic state, a linearregression test was used for a relation between the proportion of gizzard shad �200 mmLT and chlorophyll among lakes.

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T I. Mean, standard error (..), range (lowest and highest value), and coefficientof variation (CV) of parameters from the 60 Florida lakes sampled. The value PVI is the

per cent of total lake volume inhabited by aquatic macrophytes

Parameter Mean .. n Range CV

Surface area (ha) 410 230 60 2–12 000 430Depth (m) 2·9 0·2 60 <1–6 53Chlorophyll (�g l�1) 28 6·0 60 1–240 170PVI 24 4·1 60 <1–100 130Total zooplankton density(organism l�1)

3·6*105 5·5*104 60 5·5*104–2·9*106 120

RESULTS

Lakes for this study were typically shallow (mean depth <3 m) and rangedgreatly in surface area, chlorophyll concentration, PVI, and total zooplanktondensity (Table I). Based on chlorophyll concentrations, trophic state rangedfrom oligotrophic (1 �g l�1) to hypereutrophic (>40 �g l�1) according to theclassification scheme of Forsberg & Ryding (1980). Most lakes >100 ha in thisdata set were eutrophic or hypereutrophic (16 of 19, Hoyer & Canfield, 1996b),whereas about half (23 of 41) of the lakes <100 ha would be classified asoligotrophic or mesotrophic (chlorophyll values �7 �g l�1).

Gizzard shad were collected at 23 and threadfin shad at 22 of the 60 lakessampled. Gizzard shad occurrence was described best by the log10 of surfacearea (Wald �2=5·68d.f.=1, P=0·02) and the log10 of chlorophyll (Wald�2=9·11d.f.=1, P<0·01):

logit(p)=8·80�2·58[log10(surface area)]�3·23(log10chlorophyll). (3)

The independent variables arcsine (PVI) and log10total zooplankton density wereremoved from the model in the backward selection procedure. The model was94% effective in predicting gizzard shad occurrence. Probabilities of gizzard shadoccurrence increased with chlorophyll and surface area (Fig. 1).

Threadfin shad occurrence was described best by the log10 of surface area(Wald �2=9·42d.f.=1, P<0·01) and arcsine PVI (Wald �2=5·19d.f.=1, P=0·02).The logistic regression model for threadfin shad occurrence was:

logit(p)=3·92�2·23[log10(surface area)]+2·60[arcsine(PVI)]. (4)

The model was 84% effective in predicting threadfin shad occurrence (c=0·84)(Fig. 2). The independent variables log10chlorophyll and log10total zooplanktonwere not significant and were removed from the model. Generally, threadfinshad occurrence was related positively to surface area and negatively to PVI.However, the logistic model for threadfin shad (c=0·84) was a less effectivepredictor than found for gizzard shad (c=0·94).

Considering only lakes where gizzard shad or threadfin shad were collected,density, biomass and catch-per-effort in gill nets varied substantially for both

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0.00

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F. 1. Plot of predicted probability of gizzard shad occurrence on log10lake surface area andlog10chlorophyll. The overall model from logistic regression was: logit(p)=8·80�2·58[log10(surface area)]�3·23(log10chlorophyll), and p=1�elogit(p) (1+elogit(p))�1=predictedprobability of gizzard shad occurrence. The rank correlation coefficient (c) for the overall modelwas 0·94.

species (Table II). Gizzard shad and threadfin shad densities each spannedseveral orders of magnitude. Likewise, biomass values ranged from 0 to 500 kgha�1 for gizzard shad and near 0 to 150 kg ha�1 for threadfin shad. Density,biomass and catch-per-effort values of zero resulted from lakes where fish werecollected in gill nets but not block nets (n=3 for gizzard shad, n=0 for threadfinshad), or vice versa (n=2 for gizzard shad, n=15 for threadfin shad). However,density, biomass and catch-per-effort were highly variable among lakes for bothspecies, with coefficients of variation for each variable >100.

Gizzard shad abundance, biomass and catch-per-effort in gill nets increasedwith chlorophyll. The best model for gizzard shad density was:

log10(gizzard shad ha�1)= �0·09+1·76[log10(chlorophyll)]�1·48(arcsinePVI). (5)

Chlorophyll and PVI explained 65% of the variation in gizzard shad density(overall r2=0·65, n=23, P<0·01, 0·02). The independent variables log10lakesurface area and log10total zooplankton density were not significantly associatedwith gizzard shad density. Thus, gizzard shad density was associated positivelywith chlorophyll and negatively with PVI. The best model for gizzard shadbiomass (kg ha�1) was:

log10(biomass)= �0·65+1·10[log10(chlorophyll)] (6)

suggesting a positive relationship between gizzard shad biomass and chlorophyll(n=23, P<0·01, r2=0·39). Log10lake surface area, log10total zooplankton

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F. 2. Plot of predicted probability of threadfin shad occurrence on log10lake surface area and per centof lake volume inhabited by macrophytes (PVI). The overall model from logistic regression was:logit(p)=3·92�2·23[log10(surface area)]+2·60[arcsine(PVI)], and p=1�elogit(p) (1+elogit(p))�1=predicted probability of threadfin shad occurrence. The rank correlation coefficient (c) for theoverall model was 0·84.

T II. Mean, standard error (..), sample size, range (lowest and highest value), andcoefficient of variation (CV) of gizzard shad and threadfin shad density, biomass, andcatch-per-effort (fish/net night) in gill nets. Lakes where gizzard shad or threadfin shad

were not collected in either block nets or gill nets were excluded from the table

Parameter Mean .. n Range CV

Gizzard shad density (fish ha�1) 11 000 9000 23 0–200 000 390Gizzard shad biomass (kg ha�1) 57 25 23 0–500 210Gizzard shad catch-per-effort 64 20 23 0–350 150Threadfin shad density (fish ha�1) 8200 3000 22 2–65 000 170Threadfin shad biomass (kg ha�1) 22 7·0 22 <1–150 150Threadfin shad catch-per-effort 7·2 4·0 22 0–81 260

density, and arcsinePVI were nonsignificant. Thus, gizzard shad biomassincreased with chlorophyll across the lakes where they were collected (Fig. 3).Similarly, the best model for gizzard shad catch-per-effort (CPE) in gill nets was:

log10(CPE)= �0·078+0·86[log10(chlorophyll)] (7)

revealing a positive relationship between log10chlorophyll and gill net CPE(n=23, P=0·02, r2=0·25, Fig. 3). Other independent variables were non-significant.

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F. 3. Mean gizzard shad biomass (kg ha�1) (a) and gizzard shad catch per net night (b) plotted againstlog10chlorophyll. The multiple regression equations resulting from the backwards selectionprocedure are: (a) log10y= �0·65+1·10 log10x (n=23, r2=0·39; P<0·01); (b) log10y= �0·08+0·86log10x (n=23, r2=0·25, P<0·01).

Threadfin shad density increased with chlorophyll and decreased with totalzooplankton density across lakes. The best model (n=22, P<0·01, r2=0·55) forthreadfin shad density was:

log10(threadfin shad ha�1)=6·71+2·09[log10(chlorophyll)]�1·19[log10(zooplankton density)]. (8)

Thus, threadfin shad density was associated positively with chlorophyll (P<0·01)and negatively with total zooplankton density (P=0·01). Likewise, the bestmodel (n=22, P=0·02, r2=0·35) for threadfin shad biomass (kg ha�1) was:

log10(biomass)=3·62+0·88[log10(chlorophyll)]�0·72[log10(zooplankton density)]. (9)

Threadfin shad biomass increased with chlorophyll (P=0·01) but decreased withtotal zooplankton density (P=0·03). Gill net catches of threadfin shad occurredin only seven of the 22 lakes where threadfin shad were collected, which precludedanalysis of factors related to threadfin shad catch-per-effort in gill nets.

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The proportion of gizzard shad �200 mm LT generally exceeded 60% and washighly variable at chlorophyll <50 �g l�1 (Fig. 4). A relation between theproportion of gizzard shad �200 mm LT and chlorophyll could not be detectedwith regression analysis (P>0·3). Gizzard shad populations were composedgenerally of individuals that would be vulnerable to predation (i.e. �200 mmLT), but no relation was found between size structure of gizzard shadpopulations and chlorophyll.

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F. 4. The proportion of gizzard shad <200 mm LT plotted against chlorophyll concentration. Simplelinear regression was not significant (P>0·3).

DISCUSSION

No models were found relating occurrence of gizzard or threadfin shad totrophic state, lake size and aquatic vegetation abundance in the literature.Bachmann et al. (1996) conducted a study of fish community compositionamong lakes using this same data set, and they found that threadfin and gizzardshad occurrence and standing crop increased with trophic category. However,Bachmann et al. (1996) did not examine the effects of macrophytes or lake sizeon shad populations. Hoyer & Canfield (1996b) noted the importance of lakesize and trophic conditions because many of the large lakes in this data set wereeutrophic to hypereutrophic. Clearly, lake size, trophic state, and PVI wererelated to occurrence of either threadfin or gizzard shad, and the logisticregression models could be used to predict waters likely to contain gizzard shad(and possibly threadfin shad) in Florida lakes.

Changes were expected in both shad species density and biomass withchlorophyll and macrophyte abundance. Gizzard and threadfin shad densityand biomass increased with chlorophyll (trophic state) in this study as noted inprevious studies (Jenkins, 1967; Bays & Crisman, 1983; Siler et al., 1986;Bachmann et al., 1996). However, effects of PVI on density and biomass of bothshad species were variable. Gizzard shad density was related negatively to PVI,whereas PVI was not related significantly to gizzard shad biomass or threadfinshad density or biomass. Threadfin shad occurrence was associated negativelywith PVI, suggesting that threadfin shad often were not collected at lakes withhigh PVI. However, PVI was not a consistent predictor of gizzard shad and

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threadfin shad density and biomass, suggesting variable responses to PVIcompared with chlorophyll, which was related positively to both shad speciesdensity and biomass. Bettoli et al. (1993) found that gizzard shad density wasnot related to changes in macrophyte abundance due to variable recruitment.Thus, shad density and biomass may be variable in response to changes inmacrophyte abundance.

Because both shad species consume zooplankton during some or all life stages(Heidinger, 1983; Ziebell et al., 1986), density and biomass of both species wereexpected to be related positively to total zooplankton density. However, gizzardshad density and biomass were not related to zooplankton density. Contrary topredictions, threadfin shad density and biomass were inversely related to totalzooplankton density among lakes. DeVries et al. (1991) found that age-0threadfin shad reduced zooplankton abundance in one temperate lake but not ina second lake. It is possible that threadfin shad reduced zooplankton abundancein some Florida lakes in this study, which would lead to the inverse relationshipobserved. However, gizzard shad have also reduced zooplankton abundance intemperate reservoirs (Dettmers & Stein, 1992, 1996; DeVries & Stein, 1992; Steinet al., 1995; Garvey et al., 1998), yet gizzard shad density and biomass were notrelated to total zooplankton density in this study. Therefore, this study provideslittle evidence for effects of either shad on zooplankton abundance in Floridalakes. The study was a descriptive investigation of factors related to occurrence,density and biomass of both shads and did not identify mechanisms for patternsof fish distribution or abundance. Future studies should attempt to identify themechanisms for the patterns observed.

Gizzard shad and threadfin shad were not collected in the majority of Floridalakes sampled, but occurrence of both species was related positively to lakesurface area. Unlike reservoirs that are connected in a series of impoundments(Jenkins, 1967; DiCenzo et al., 1996), many Florida lakes are isolated individu-ally (Brenner et al., 1990), probably reducing the dispersion of both spad speciesamong Florida lakes relative to temperate reservoirs. The scarcity of both shadspecies in small, oligotrophic and mesotrophic lakes warrants further investiga-tion, particularly because potential interactions between piscivores and eithershad species in small lakes with chlorophyll �7 �g l�1 were not evident fromthis study. Management strategies regarding either shad species would bewarranted in primarily large (>100 ha), fertile (chlorophyll >7 �g l�1) Floridalakes.

Nevertheless, eutrophic and hypereutrophic lakes may be most likely to havegizzard shad or threadfin shad alter limnological variables and the fish commu-nity. Drenner et al. (1996) used mesocosm experiments to demonstrate thatgizzard shad effects on phytoplankton abundance, nutrient concentrations, andzooplankton density increased with trophic state. Thus, gizzard and threadfinshad may be more likely to alter the lake ecosystem in eutrophic andhypereutrophic Florida lakes compared to less-fertile lakes. The shads may alsoaffect recruitment of sport fishes via interactions during early life by reducingzooplankton abundance (DeVries et al., 1991; Dettmers & Stein, 1992; DeVries& Stein, 1992). Early-life interactions between both shad species and sport fisheswarrant further investigation in Florida lakes, particularly because of the longgrowing season compared with more northern systems and because chlorophyll

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concentrations and shad biomass values span orders of magnitude. Addition-ally, several studies (Jenkins, 1967; Bays & Crisman, 1983; Bachmann et al.,1996; Ney, 1996) have shown that the per cent contribution of planktivorousfishes to the total fish biomass increases with trophic state. Because both shadspecies were found primarily in eutrophic and hypereutrophic lakes in this study,potential influences of shad on other fishes during early life may be high in lakeswhere they occurred. However, potential influences of the shads on waterchemistry and fish community dynamics have been investigated only in morenorthern reservoirs and have not been evaluated for lakes or reservoirs at thesouthern latitude of lakes in this study.

DiCenzo et al., 1996 found that the proportion of gizzard shad <200 mm LT

determined with electrofishing increased asymptotically with chlorophyll inAlabama reservoirs and neared 100% as reservoir chlorophyll values exceeded25 �g l�1. Gizzard shad in this study were not collected in any oligotrophiclakes (chlorophyll <3 �g l�1) and were collected in only two mesotrophic lakes(chlorophyll 3–7 �g l�1). Thus, the range of chlorophyll where gizzard shadwere collected in this study (4–241 �g l�1) was larger than reported for Alabamareservoirs (range 2–27 �g l�1, DiCenzo et al., 1996). Although most lakescontaining gizzard shad had a high proportion of gizzard shad �200 mm LT (asexpected from DiCenzo et al., 1996) there were exceptions. For example, LakeWauberg had a chlorophyll of c. 100 �g l�1 but <10% of fish were �200 mmLT. Nevertheless, only four lakes contained gizzard shad where <60% were�200 mm LT, suggesting that generally eutrophic and hypereutrophic Floridalakes contained gizzard shad populations that were mostly �200 mm LT, asfound by DiCenzo et al. (1996).

Because gizzard shad often grow beyond sizes where they are available topredators (Noble, 1981), fishery managers have attempted frequently to elimi-nate or reduce gizzard shad populations to improve sport-fish populations(DeVries & Stein, 1990; Hale, 1996). In this study, gizzard shad were probablyvulnerable to predation in most of the eutrophic and hypereutrophic lakes wherethey occurred. Thus, low vulnerability of gizzard shad to predators appears rarein eutrophic and hypereutrophic Florida lakes. Although gizzard shad appearedto be a suitable prey species for adult predators in most lakes where gizzard shadwere collected, potential negative impacts of gizzard shad on age-0 sport fisheshave not been explored for lakes at this latitude.

This research was funded by the Bureau of Aquatic Plant Management (Contractnumber C 3748), Florida Department of Environmental Protection. We thank all peoplewho assisted in field collections; D. Parkyn for assistance with Figs 1 and 2; and T. Cross,D. DeVries, M. Hansen, M. Maceina, and G. Wilde for comments on a previous draft ofthis manuscript.

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Dettmers, J. M. & Stein, R. A. (1996). Quantifying linkages among gizzard shad,zooplankton, and phytoplankton in reservoirs. Transactions of the AmericanFisheries Society 125, 27–41.

DeVries, D. R. & Stein, R. A. (1990). Manipulating shad to enhance sport fisheries inNorth America: an assessment. North American Journal of Fisheries Management10, 209–223.

DeVries, D. R. & Stein, R. A. (1992). Complex interactions between fish andzooplankton: quantifying the role of an open-water planktivore. Canadian Journalof Fisheries and Aquatic Sciences 49, 1216–1227.

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