Download - Larval fishes of a Middle Atlantic Bight estuary: assemblage structure and temporal stability

Larval fishes of a Middle Atlantic Bight estuary:assemblage structure and temporal stability

David A. Witting, Kenneth W. Able, and Michael P. Fahay

Abstract: We collected weekly, quantitative ichthyoplankton samples over 6 years (1989–1994, 1309 samples) toidentify temporal scales of variability in the abundance and occurrence of larval fish assemblages near Little Egg Inletin southern New Jersey, U.S.A. We collected species that spawn in the estuary (30%), both the estuary and continentalshelf (35%), continental shelf (25%), and the Sargasso Sea (10%). The following analyses suggest an annually repeatedseasonal progression of species assemblages: (i) the rank abundance of the 20 dominant species did not changesignificantly from year to year, (ii ) variation in the density of the dominant species was primarily explained byintraannual rather than interannual variation, and (iii ) multivariate analysis of the assemblage matrix identified fiveseasonal assemblages that occurred during all six years. We found that the timing and duration of each of theseseasonal groups were correlated with two characteristics of the annual temperature cycle, magnitude (higher or lowertemperature) and trajectory (increasing vs decreasing temperature). We suggest that the repeated occurrence of larvalfish assemblages in temperate estuaries along the U.S. coast may, in part, be driven by local environmental processes.

Résumé: Nous avons prélevé hebdomadairement des échantillons quantitatifs d’ichtyoplancton durant 6 ans(1989–1994, 1 309 échantillons) pour établir les échelles temporelles de variabilité de l’abondance et de la présence degroupements de larves de poissons près du bras Little Egg dans le sud du New Jersey, aux États-Unis. Nous avonsrecueilli des espèces qui frayent dans l’estuaire (30%), à la fois dans l’estuaire et sur la plate-forme continentale(35%), sur la plate-forme continentale seulement (25%) et dans la mer des Sargasses (10%). Les résultats suivantslaissent penser qu’il y a une progression saisonnière répétée annuellement des groupements d’espèces : (i) lesabondances relatives des 20 espèces dominantes n’ont pas changé significativement d’année en année, (ii ) la variationde la densité de l’espèce dominante s’expliquait principalement par une variation intra-annuelle plutôt qu’inter-annuelleet (iii ) l’analyse multivariée de la matrice des groupements a permis de repérer cinq groupements saisonniers quiétaient présents durant les six années. Nous avons observé que le moment de formation et la durée de chacun de cesgroupements saisonniers étaient corrélés avec deux caractéristiques du cycle annuel de la température, soit le degré detempérature (température basse ou élevée) et la tendance de la température (température croissante ou décroissante).Nous pensons que la survenue répétée des groupements de larves de poissons dans les estuaires tempérés de la côteaméricaine peut en partie être déterminée par des processus environnementaux locaux.

[Traduit par la Rédaction] Witting et al. 230

The orderly seasonal succession in abundance and speciescomposition of the dominant components of temperate estu-arine assemblages figures prominently in all general reviews(Allen and Barker 1990; McGovern and Wenner 1990). Geo-graphical variation in sources of larvae is also a commoncharacteristic of many estuarine fish assemblages (Frank andLeggett 1983; Suthers and Frank 1991; Hettler and Barker1992). There are, however, few published examples thatdemonstrate the repeatability of species assemblages andabundances from year to year. A large part of these seasonal

changes have been attributed to the transient nature of thedominant species such that the estuary is only used duringspecific life history stages (McGovern and Wenner 1990).Others have suggested that spawning is timed such that thearrival of early life history stages into the nursery groundstakes advantage of favorable conditions relative to feeding,predation, etc. (e.g., Frank and Leggett 1983), thus produc-ing a seasonal pattern in the species composition of larvaeentering the estuary. In either case, long-term data that testthe pattern of repeated progression of seasonal assemblagesare largely unavailable.

In this paper, we consider larval supply and the temporalstability of estuarine fish assemblages by examining an ich-thyoplankton assemblage in the polyhaline portion of a south-ern New Jersey, U.S.A., estuary with weekly sampling over6 years. In this estuary, as in others, each year of observa-tions involves a new year-class of all species involved; thus,the influence of interannual variation in larval supply can beclearly identified. If the larval fishes occur in repeated, pre-dictable sequence, we may infer that larval supply maystabilize an annual pattern of species abundances. Alterna-tively, if the seasonal sequence of species occurrence variesfrom year to year, or if larval absolute and rank abundances

Can. J. Fish. Aquat. Sci.56: 222–230 (1999) © 1999 NRC Canada

222

Received April 27, 1998. Accepted October 8, 1998.J14559

D.A. Witting 1 and M.P. Fahay. NOAA/National MarineFisheries Service, James J. Howard Marine SciencesLaboratory, 74 Magruder Rd., Sandy Hook, NJ 07732, U.S.A.K.W. Able. Marine Field Station, Institute of Marine andCoastal Sciences, Rutgers University, 800 Great Bay Blvd.,c/o 132 Great Bay Blvd.,Tuckerton, NJ 08087-2004, U.S.A.

1Author to whom all correspondence should be addressed.e-mail: [email protected]

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vary dramatically from year to year, we can infer that larvalsupply is a source of interannual variation in the seasonalprogression of species.

Study areaThe Great Bay – Little Egg Harbor estuarine system (Fig. 1) is

polyhaline and shallow (average depth 1.7 m). It comprises adrowned river valley (Mullica River), embayment (Great Bay), andadjacent barrier beach estuary (Little Egg Harbor). A natural inlet(Little Egg Inlet) is the primary source of ocean water entering thisestuary. Several thoroughfares or “creeks”, including Little Sheeps-head Creek, run through a peninsula and serve to connect GreatBay and Little Egg Harbor. This estuary shares many characteris-tics with other estuaries in the Middle Atlantic Bight including abroad seasonal temperature range (–2 to 28°C) and a moderatetidal range (about 1 m) (Able et al. 1992). The fauna of residentand migratory fishes is enriched by northern and southern migrantspecies (Able and Fahay 1998).

All samples of larval and early juvenile fishes, hereafter referredto as larvae, were collected from a bridge that spans Little Sheeps-head Creek (Fig. 1). The bridge is located 3 km from the creekmouth, 2.5 km from Little Egg Inlet. Water depth at the samplinglocation is about 4 m. Atlantic Ocean water flows into the estuarythrough Little Egg Inlet during flood tides, some directly into themouth of Little Sheepshead Creek.

Sampling protocolWe collected samples weekly by suspending a 1-m-diameter

(1-mm mesh) plankton net from the bridge during night flood tides(referred to hereafter as a deployment). Five 30-min deploymentsof two nets, one at the surface and one near the bottom, produced atotal of 10 deployments per sampling date beginning in February1989. Between May 1990 and July 1991, we made three deploy-ments of two nets at these same depths, and between August 1991and November 1994, we made three deployments of one net half-way between surface and bottom (midwater). There was no shift inspecies abundance (relative, absolute, or rank) in the year that wechanged from paired surface and bottom deployments to a singlemidwater deployment. To estimate the volume of water sampled,we fixed a General Oceanics flowmeter in the mouth of the net.We made a total of 42 deployments without flowmeters between 1and 17 February 1989 and 1309 deployments with flowmetersbetween 20 February 1989 and 30 November 1994. We did notinclude samples from deployments that were made without flow-meters in any analyses of density. We sampled an average of 401 m3

(150 m3 SD) of water in each tow, and a small fraction (5%) of de-ployments sampled less than 100 m3. At the beginning and end ofeach sampling effort, we measured surface water temperature witha field thermometer and salinity with a refractometer. To determinelong-term interannual variation in temperature, we also incorpo-rated a data set of daily water temperatures recorded in the estuaryspanning 18 years (Able et al. 1992).

After each deployment, we sorted all samples in the laboratoryby placing small portions of the samples in shallow pans and re-moving all fish, which were then preserved in 95% ethanol. Wethen identified, measured, counted, and assigned a notochord flex-ion stage to each preserved fish. We used notochord flexion tocharacterize the onset of the transition from larval (preflexion) tojuvenile (postflexion) life history stages. Lengths were also used toindicate ontogenetic state; however, we consider flexion stage tobe a better indicator than length due to the potentially confoundingeffects of shrinkage that may occur due to preservation. We consid-ered larvae to be in the preflexion stage if the notochord wasstraight at the caudal tip, flexion stage if the notochord flexed dor-sally at the caudal peduncle and hypural formation had begun, andpostflexion stage if the hypural plate was fully formed.

There were a few taxonomically confusing forms. For example,Ammodytesmay be represented by two species, but was most likelyAmmodytes americanusbecause of the estuarine sampling location(Nizinski et al. 1990). In addition, we did not resolve the identifi-cation of two species of goby (Gobiosoma boscand Gobiosomaginsburgi) until the third year of sampling (Duval and Able 1998);thus, we combined them asGobiosomaspp. for the 1989 and 1990samples.

Data analysisWe used nested ANOVA (Hicks 1993) to partition the temporal

variation in density of each of the 20 most dominant species intointerannual, intermonth, and interdate variation, testing for the con-tribution of each temporal scale of variability (year, month withinyear, and date within month and year) to the total variation in thedensity. The density estimates for each species were log trans-formed prior to analysis, and density estimates from each deploy-ment were used as the sampling unit. We included only the monthsthat a species was collected (all years combined) and examinedinteryear versus intrayear variation within and between these months;we first established the months during which each species was col-lected by averaging the densities and generating 95% confidenceintervals and then included densities within these confidence lim-its. After conducting the nested ANOVA, we also calculated thefraction of the variation explained by each temporal scale of vari-ability from the total variation explained by the model (R2) and theaverage density and the coefficient of variation (CV) for each spe-cies collected.

We conducted analyses that tested for temporal consistency in

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Witting et al. 223

Fig. 1. Great Bay – Little Egg Harbor estuary in southern NewJersey with the sampling location at Little Sheepshead Creeknear Little Egg Inlet.



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224 Can. J. Fish. Aquat. Sci. Vol. 56, 1999

SpeciesTotalno.

Cumulative%

Spawningarea

No.staged

Preflexion(%)

Flexion(%)

Postflexion(%)

Anchoa mitchilli 105 117 77.6 E, C 4 399 6.3 27.3 66.2Syngnathus fuscus 7 056 82.8 E 2 165 0 0.1 99.2Menidia menidia 4 486 86.1 E 1 434 11.3 24.2 64.2Pleuronectes americanus 3 750 88.9 E 1 801 6.9 84.1 8.7Ammodytessp. 2 352 90.6 C 1 018 6.0 34.2 59.8Anguilla rostrata 2 238 92.2 SS 1 359 0 0 100Clupea harengus 1 427 95.1 C 1 047 0 0.7 99.3Gobiosoma bosc 1 082 95.9 E 607 0 3.6 96.4Fundulus heteroclitus 942 96.6 E 246 0.4 0 99.6Brevoortia tyrannus 870 97.2 E, C 623 0.2 2.4 97.4Etropus microstomus 724 97.8 C 445 0 0 100Paralichthys dentatus 671 98.3 C 433 0 0 100Scophthalmus aquosus 434 98.6 E, C 306 27.1 39.5 31.1Gasterosteus aculeatus 346 98.9 E 199 0 0 100Tautoga onitis 289 99.1 E, C 131 0 21.4 78.6Conger oceanicus 284 99.3 SS 163 0 0 100Micropogonias undulatus 244 99.5 C 185 0 16.2 83.8Gobiosoma ginsburgi 235 99.7 E, C 235 0.4 0.9 98.7Gobionellus boleosoma 222 99.8 E, C 163 0 0 100Cynoscion regalis 203 100 E 127 6.3 15 68.5Total 132 972 17 086 3.2 14.8 82.5

Note: Spawning areas are estuaries (E), continental shelf (C), or Sargasso Sea (SS) based upon Able and Fahay (1998).

Table 1. Sample size and flexion stage for the 20 most abundant forms collected from Little Sheepshead Creek inthe vicinity of Little Egg Inlet.

Species R2Date within monthand year (%)

Month withinyear (%)

Amongyears (%)

Monthrange

Averagedensity

Interyear(CV)

Anchoa mitchilli 0.75*** 32.5*** 62.4*** 5.2 ns 6–11 216.730 78.10Syngnathus fuscus 0.76*** 28.3*** 71.7*** <0.1 ns 3–7 15.927 87.70Menidia menidia 0.76*** 47.1*** 52.9*** <0.1 ns 4–12 9.079 86.10Pleuronectes americanus 0.69*** 54.5*** 45.5** <0.1 ns 4–5 8.445 61.6Ammodytessp. 0.72*** 48.7*** 51.3*** <0.1 ns 3–5 4.203 72.20Anguilla rostrata 0.59*** 25.6*** 74.4*** <0.1 ns 11–6 3.17 69.70Clupea harengus 0.71*** 23.8*** 13.3*** 62.9*** 1–4 2.53 153.30Gobiosoma bosc 0.63*** 68.4*** 31.6* <0.1 ns 6–11 3.009 51.50Fundulus heteroclitus 0.54*** 77.6*** 22.0** 0.5 ns 1–12 1.156 137.40Brevoortia tyrannus 0.54*** 65.4*** 34.6*** <0.1 ns 8–6 1.374 39.10Etropus microstomus 0.60*** 64.4*** 34.7** 0.9 ns 7–11 1.698 83.90Paralichthys dentatus 0.57*** 46.1*** 48.7*** 5.3 ns 10–6 1.031 70.00Scophthalmus aquosus 0.77*** 79.6*** 13.7 ns 6.8 ns 5–7 0.943 97.40Gasterosteus aculeatus 0.54*** 79.3*** 20.7** <0.1 ns 8–6 0.462 63.50Tautoga onitis 0.79*** 79.6*** 7.6 ns 12.8 ns 6–7 0.730 214.20Conger oceanicus 0.56*** 88.9*** <0.1 ns 11.1 ns 5–6 0.502 84.50Micropogonias undulatus 0.44*** 70.8*** 29.2* <0.1 ns 9–12 0.414 77.60Gobiosoma ginsburgi 0.50** 36.2 ns <0.1 ns 63.8* 7–8 0.698 129.60Gobionellus boleosoma 0.71*** 57.9*** <0.1 ns 42.1*** 9–11 0.365 213.70Cynoscion regalis 0.61*** 92.8*** 7.2 ns <0.1 ns 6–8 0.472 86.70

Note: R2 refers to the proportion of the variation in the density of each species explained by each level. Date within month and yearrefers to the percentage of the variation explained by variation among dates, month within year refers to the percentage of the variationexplained by variation among months, and among years refers to the percentage of the variation explained by differences between years.ns, not significant (p > 0.05); *p < 0.05; **p < 0.01; ***p < 0.001.

Table 2. Results of nested ANOVA testing for the contribution of years, months within years, and dates within monthsand years to the total variation in density of the 20 species most frequently collected from Little Sheepshead Creek inthe vicinity of Little Egg Inlet.



assemblage structure using rank correlation analysis (Sokal andRohlf 1981). We ranked the 20 dominant species in order of theirabundance for each year from highest to lowest and then generateda correlation matrix that tested the null hypothesis that the rankingof a species in one year was not correlated with its ranking in an-other year.

To identify seasonal assemblages of species, we used clusteranalysis. We used a faunal distance metric, cord normalized ex-pected species shared (CNESS) (Trueblood et al. 1994), which isrelated to both Orloci’s (1978) chord distance and Grassle andSmith’s (1976) faunal similarity index (normalized expected spe-cies shared (NESS)), as a transformation that balanced the contri-bution of rare and dominant species. This index circumvents theassumption of a specific distribution of individuals among speciesin nature and the dependence upon sample size (Sanders et al.1980). The probabilities of sampling speciesk from samplei givena random draw ofm individuals from samplei were calculated by

HN N CmN Cm

ik mi ik

i|

[( ) ]( )

= − −1

where [Ni, C, m] is the combination function, or the number ofunique ways of samplingm objects from a sample ofNi individu-als; Ni was calculated by

[ , , ]!

[ !( )!]N C m

Nm N m

ii

i

=−

The Euclidian distance between samples in the centered, normal-ized H matrix (Pielou 1984) is the CNESS index. Finally, we usedgroup-averaged cluster analysis (Pielou 1984) to group the samplesinto seasonal assemblages (Trueblood et al. 1994). To analyzethese data, we calculated CNESS using anm size of 10.

To determine the relationship between the timing of each sea-sonal assemblage (as defined by cluster analysis) and temperature,we used smoothed daily surface temperatures from the estuary. Wecalculated an average and a daily rate of change from the dailytemperatures recorded for the 10 days prior to each day of sam-pling. With these two measures of temperature (magnitude and tra-jectory), we identified the occurrence of each assemblage type over

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Witting et al. 225

Fig. 2. (a) Composite length–frequency distribution of all fish(n = 22 795) measured from ichthyoplankton collections fromLittle Sheepshead Creek in the vicinity of Little Egg Inlet.Lengths are all standard length except for Anguilliformes, whichare total length. (b) Frequency distribution of interyear CV forall species collected.

Fig. 3. Density plotted against date for five fish species collectedfrom Little Sheepshead Creek in the vicinity of Little Egg Inlet.(a) Summer estuarine spawner (Anchoa mitchilli); (b) springestuarine spawner (Pleuronectes americanus); (c) Sargasso Seaspawner, winter immigrant (Anguilla rostrata); (d) shelf spawner,winter immigrant (Clupea harengus); (e) summer estuarinespawner (Fundulus heteroclitus).



the seasonal pattern in temperature. For example, spring may besimilar to fall in magnitude, but opposite in trajectory. We used atwo-factor ANOVA to test for the relationship between year, as-semblage type, mean preceding temperature, and trajectory of tem-perature change.

Environmental patternsThe seasonal pattern of water temperatures in spring,

summer, and fall was consistent among years. Temperatureranged from –2 to 27°C (annual average 12.7°C) during the6 years of sampling. The timing of temperatures risingabove the annual average in the spring and falling below theannual average in the fall was always within 15 days of 1May and 1 November. Summer temperatures typicallypeaked at 20–25°C, with very little interyear variability inthe pattern. Winter temperatures dropped to about 0°C andshowed higher levels of interannual variability.

Salinity ranged from 22 to 32‰ over 6 years, with an av-erage salinity of 28.6‰ (±0.16 SE). Salinity did not show aconsistent seasonal pattern, but did vary significantly withinsampling periods. Salinities prior to the first deployment ofthe net on each date were significantly lower than those afterthe last deployment (27.7 versus 29.3‰) (ANOVA,F =31.4,p = 0.0001), indicating that higher-salinity ocean waterwas entering the sampling site with the flooding tide.

Species and life history stage compositionA total of 135 528 fish representing 81 species in 43 fami-

lies were collected during the sampling period. The domi-nant families were Bothidae (six species), Gadidae (five),and Sciaenidae (five). The most abundant species wasAnchoa mitchilli (77.6% of all fish collected). Five speciesmade up 90.6% of all larvae, and the top 20 species made up98.1% (Table 1). Estuarine species that were absent fromthese collections were those with demersal larvae (e.g.,Opsanus tau) and those that occur primarily in the oligo-haline portions of the estuary (e.g.,Trinectes maculatus).

Fish of all life history stages (larval to adult) from 2 to250 mm were collected during the study (Fig. 2a). The ma-jority, however, were larvae and early juveniles between 4and 75 mm. The aggregate length–frequency distribution re-vealed two distinct modes. The first mode, at about 10 mm,comprised primarilyPleuronectes americanus(28%) andA. mitchilli (21%). A second mode, at about 60 mm, com-prisedAnguilla rostrata “glass eels” andConger oceanicuslate leptocephali (68%).

All flexion stages were observed in the dominant species(Table 1). Overall, the collections were composed primarilyof postflexion individuals (82.5%), with most of the othersundergoing flexion (14.8%) and few in the preflexion stages.There was considerable variation between species in the pro-portion of each stage that was collected. Of the 20 most fre-quently captured forms,Gobionellus boleosoma, G. bosc,G. ginsburgi, Clupea harengus, Brevoortia tyrannus, Fundu-lus heteroclitus, Gasterosteus aculeatus, Paralichthys denta-

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Species 1989 1990 1991 1992 1993 1994 Mean ± SE

Anchoa mitchilli 1 1 1 1 1 2 1.17±0.17Syngnathus fuscus 2 2 4 2 2 1 2.17±0.40Pleuronectes americanus 4 4 2 4 5 6 4.17±0.54Gobiosomaspp. 5 5 5 3 4 7 4.83±0.54Menidia menidia 3 8 3 7 9 3 5.50±1.15Ammodytessp. 6 3 6 11 12 4 7.00±1.51Anguilla rostrata 7 7 7 9 7 5 7.00±0.52Clupea harengus 8 16 10 5 3 9 8.50±1.84Etropus microstomus 9 14 9 8 6 12 9.67±1.17Brevoortia tyrannus 10 9 11 10 13 13 11.00±0.68Paralichthys dentatus 12 11 8 13 16 10 11.67±1.12Fundulus heteroclitus 11 6 18 15 15 14 13.17±1.70Scophthalmus aquosus 13 18 17 16 8 8 13.33±1.82Conger oceanicus 16 15 16 18 10 11 14.33±1.28Anchoa hepsetus 15 10 13 19 11 20 14.67±1.69Tautoga onitis 14 17 20 6 20 16 15.50±2.13Cynoscion regalis 17 20 14 12 18 15 16.00±1.18Gasterosteus aculeatus 18 12 15 17 17 17 16.00±0.89Micropogonias undulatus 19 13 19 14 14 19 16.33±1.20Gobionellus boleosoma 20 19 12 20 19 18 18.00±1.24

1994 0.89 0.9 0.64 0.79 0.69 0.731993 0.78 0.78 0.55 0.67 0.651992 0.83 0.83 0.53 0.661991 0.84 0.84 0.691990 0.78 0.78

Table 3. Rank abundance for each year and average rank for the 20 most common species collected in LittleSheepshead Creek in the vicinity of Little Egg Inlet and year-by-year correlation matrix for rank speciesabundance.



tus, Etropus microstomus, andSyngnathus fuscuswere almostentirely postflexion larvae or juveniles. In six of the abun-dant species (A. mitchilli, Menidia menidia, P. americanus,Ammodytessp., Tautoga onitis, and Micropogonias undu-latus), a large fraction of the individuals were undergoingflexion. In contrast with most of the abundant species col-lected,Scophthalmus aquosuswere collected at all flexionstages.

Temporal stability and assemblage structureMost of the 20 dominant species showed higher levels of

variability within years than between years (nested ANOVA)(Table 2). Dominant species typically occurred as a pulse,reaching similar maximum densities in each year (Fig. 3).This pattern was true for species that spawn locally as wellas for those that spawn far from the estuary (Figs. 3a–3c). Inall but G. ginsburgi, the component of the variation in den-sity explained by sampling dates within years was highlysignificant (Table 2). CV’s for interyear abundance (calcu-lated for all species represented by more than 10 individu-als) varied considerably among species and ranged from 39to 245% (Fig. 2b). The distribution of CV’s was lognormal(Wilk–Shapiro, W = 0.96, p > 0.10). Of the 20 most fre-quently captured forms, all but five (G. ginsburgi,C. harengus, G. boleosoma, T. onitis, and F. heteroclitus)had CV’s that were lower than the 50th percentile of thelognormal distribution. Other forms showing lower interyearCV’s were Prionotus carolinus, Prionotus evolans, O. tau,and Myrophis punctatus. Of the five species that showedhigher CV’s (see above), four also had higher variance com-ponents at the interyear temporal scale. For example, inter-year variation explained a higher (12.8%) but nonsignificantportion of the variation inT. onitis. Clupea harengusin-creased in abundance each year during the sampling period(Fig. 3d) andG. boleosomawas absent in one year, leadingto greater interyear variation than intrayear variation. Thefifth species with a high CV (F. heteroclitus) was collectedin extreme abundance on a single day in 1990 (Fig. 3e).

Larval assemblage structure was consistent from year toyear (Table 3). The rankings for the 20 most common spe-cies for all years were significantly correlated (p < 0.05).Correlation coefficients for year-by-year comparisons rangedfrom 0.53 to 0.90 and were normally distributed (Wilk-Shapiro,p > 0.5). In addition, there is no evidence of changeover the 6-year time series (each year was equally similar toadjacent years as to more distant years). Most species werecollected in each year. Seventeen of the 20 dominant species

were collected in all six years. Thirty species (31.6%) werecollected in either five or six years. Twenty- eight species(29.5%) were collected in only one year. These were all rarespecies, and 24 of them were represented by a single speci-men.

CNESS similarity and group average analyses resulted infive distinct seasonal groups and five samples that were dif-ferent (referred to hereafter as outliers) from the main clus-ters (Fig. 4). Four of the five seasonal assemblage occurredin each year (Fig. 5). Outliers (designated O1–O5) arose be-tween seasons and represented seasonal transitions (Fig. 4).The only exception to the general seasonal progression oc-curred in 1993, when the fall assemblage E did not occur.

A unique assemblage of species dominated each majorcluster/assemblage.Clupea harengusand A. rostrata domi-nated the late winter assemblage A, with lower numbers ofAmmodytessp., P. dentatus, and B. tyrannus. Pleuronectesamericanusand Ammodytessp. dominated the early springassemblage B, in addition to lower numbers ofC. harengus,A. rostrata, andB. tyrannus. Menidia menidia, S. fuscus, andA. mitchilli dominated the late spring assemblage C, withsmaller numbers ofS. aquosus. Anchoa mitchillidominatedthe summer assemblage D that made up 96% of the totalnumber of larvae in the summer samples. Finally, the fall as-semblage E was the most diverse group, with strong repre-sentation byB. tyrannus, M. undulatus, P. dentatus, andA. mitchilli and smaller numbers ofM. punctatus, Urophycisregia, Symphurussp.,Fundulus majalis, E. microstomus, andA. rostrata. Rare species composed a larger fraction of thefall group (6.7%) than any of the other seasonal assemblages(0.6–2.2%).

Temperature influenced the timing and duration of the as-semblages. The average temperature varied significantlyamong the six years sampled, largely due to interannualvariation in winter temperatures. Mean temperatures and

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Witting et al. 227

Fig. 4. Results of cluster analysis of CNESS similarities thatidentify five distinct seasonal assemblages (A–E) and fivesmaller outliers (O1–O5).

Fig. 5. Seasonal progression of species assemblages for each ofthe six years sampled. Each cluster type is represented by adifferent symbol. Assemblages, based upon results of clusteranalysis, include major (A (u), B (d), C (w), D (:), E (V))and outlier (s) assemblages. Note the consistent seasonalprogression among years and the timing of outlier assemblagesduring the transitional periods between major assemblages.



temperature trajectories varied significantly among the sixassemblage types identified by the cluster analysis (Fig. 6).The year by assemblage interaction was not significant. Mul-tiple comparisons (SNK) tests demonstrated that each sea-sonal assemblage occurred during a unique combination oftemperatures and temperature trajectories (Fig. 6). Wheremean preceding temperatures were similar between assem-blage types (B versus E), the rate of temperature change wassimilar in magnitude, but opposite in direction (Fig. 6). Whenthe rate of temperature change was similar in direction andmagnitude (B versus C), the average preceding temperatureswere significantly different (Fig. 6).

Life history stage and species compositionThe species composition of larval fishes that we collected

in this estuary was characteristic of the juveniles and adultsthat are collected in the polyhaline portion of the system(Able and Fahay 1998). The size and stage information forthe dominant species indicates two general categories of fishes.The first is characterized by abundant smaller, earlier-stagelarvae that were often caught over a larger size range aswell. These are primarily local spawners (e.g.,P. americanus,A. mitchilli, andS. aquosus). The second is characterized bytypically larger, later-stage fishes that either spawn fartheroutside the estuary and are transported there (e.g.,A. rostrata,C. oceanicus, C. harengus, B. tyrannus, andP. dentatus) orhatch at an advanced stage of development(e.g.,F. heteroclitus,

S. fuscus, andHippocampus erectus). The dominance of post-flexion stages for other species that spawn in the estuary(e.g.,G. boscandG. boleosoma) is explained by transport ofnewly hatched larvae soon after hatching to low-salinity por-tions of the estuary (Massmann et al. 1963; Shenker et al.1983) and by net mesh selectivity.

The species that entered the estuary from distant spawn-ing locations were doing so during the transformation to thejuvenile stage; thus the shift from oceanic to estuarine habi-tats was coupled with a morphological change from larva tojuvenile. These species represented a diversity of taxa.ClupeaharengusandB. tyrannusentered the estuary largely as late-larval, postflexion individuals. Similarly, two of the abun-dant flatfish, E. microstomusand P. dentatus, entered theestuary at a narrow size range as postflexion larvae and pri-marily after the onset of eye migration.Conger oceanicusentered the estuary as late-stage leptocephali andA. rostrataas glass eels. Estuarine entry during the transition from thelarval to juvenile stage is characteristic of many estuarineassemblages of the South Atlantic Bight (Hoss and Thayer1993), the Gulf of Mexico (Yanez-Arancibia et al. 1985),Spain (Arias and Drake 1990), South Africa (Beckley 1986),and Australia (West and King 1996). We collected a largerrange of stages of more locally spawned species because oftheir greater availability at all stages of development. Thebest examples areS. aquosusand M. menidia, but this wasalso true forP. americanus, Ammodytessp., andA. mitchilli.

Each analysis indicates a distinct seasonal progression inspecies composition. The annual pattern in species assem-blages is highly correlated with temperature. Each segmentof the seasonal temperature curve likely contributes uniquehabitat attributes (dissolved oxygen levels, primary produc-tivity, species and size distribution of predators, etc.), thuscreating temporal partitioning of resources (sensu DeAngelisand Waterhouse 1987). Similarly, strong seasonal patterns inspecies composition have also been identified in systemswhere the seasonal temperature variation is greater than theinterannual variation. Warlen and Burke (1990) found a con-sistent winter and spring assemblage of larval fishes in aNorth Carolina estuary that were similar to the fall, winter,and spring collections reported here, withB. tyrannus,P. dentatus, M. undulatus, and A. rostrata being the domi-nant species. Several studies conducted in North Carolina(e.g., Warlen 1994) and in New Jersey found that larvalB. tyrannusappeared in the estuaries during the fall andspring, as they did in our samples.Brevoortia tyrannusis animportant component of two seasonal assemblages, fall andlate winter. This may be a result of two groups of larvae en-tering the estuary, those that are spawned locally arriving inthe fall and those that are spawned more distantly arriving inthe spring (S.M. Warlen, K.W. Able, and E. Laban, un-published data). The Great South Bay (New York) springand summer fish larval species composition was similar tothose presented here for the Great Bay – Little Egg Harborwith dominance of anchovies, gobies, syngnathids,atherinids, and labrids in the summer andA. americanus,P. americanus, andS. aquosusamong the dominant speciesin the fall (Monteleone 1992). In our study, and others con-ducted in estuaries in the western North Atlantic Ocean(e.g., Pearcy and Richards 1962; Vouglitois et al. 1987),A. mitchilli is the dominant species during the summer.

© 1999 NRC Canada


Fig. 6. Results of an SNK multiple range test that tested fordifferences among major assemblages (A–E on the horizontalaxis) in (a) average temperature and (b) average temperaturetrajectory. Bars labeled with similar letters are not significantlydifferent.



Temporal stability in assemblage structureOur analyses of the larval fish assemblages of the Great

Bay – Little Egg Harbor estuary suggest a high degree oftemporal predictability at seasonal and annual scales. A pre-dictable seasonal progression of species assemblages reoc-curred in each of the six years studied. Almost all of thedominant species not only occurred during all years sam-pled, but their rank order of abundance was also consistentamong years. Furthermore, our analysis of variation in thedensities of individual species also suggests that interannualpatterns in the Great Bay – Little Egg Harbor estuary ap-peared to be predictable. In general, interannual variabilitydid not contribute significantly to the total variation in den-sity of most of the abundant fish species (Table 2).

Three species (C. harengus, G. boleosoma, andG. ginsbergi)varied significantly in density among years.Gobiosoma gins-burgi was combined withG. bosc for the first 2 years ofsampling so that there were fewer years of data for this spe-cies; this smaller sample size may explain the increasedinterannual variation.Clupea harengusand G. boleosomarepresent northern and southern species that are at the ex-tremes of their ranges. In addition, interannual variation inC. harengusmay be a result of the dramatic increase inwestern Atlantic stocks of this species (Smith and Morse1990), which results in a consistent increase in abundanceduring the six years that we sampled (Fig. 3d). There wasalso a notable difference between the relatively high inter-year CV (137.4%) and the relatively small interyear variancecomponent forF. heteroclitus. It is likely that the high CVfor F. heteroclitusis probably due to the large numbers caughton 6 July 1990 (>500, 10 times as many as on any other date(Fig. 3e)), resulting in an increase in variation among dateswithin years, rather than among years.

Only a few studies have attempted to quantify interyearvariability in abundance of larval fishes. This is becausemost larval surveys span about 1 year (but see Allen andBarker 1990). Longer-term studies are becoming more widelyrecognized as important in understanding estuarine processes(Wolfe et al. 1987) where the importance of understandingthe temporal scale within which variation occurs is empha-sized (Livingston 1987). Monteleone et al. (1987) attemptedto determine the interyear variability inA. americanusovera 32-year period by combining a large number of samplingprograms. Their sampling was inconsistent throughout theperiod, creating gaps in the data; thus, they were unable topartition inter- and intra-year variability. In the only studysimilar to the one presented here, Allen and Barker (1990)also determined that interannual variability over a 5-year pe-riod did not contribute significantly to the total variability ofmost of the larval fishes in the North Inlet estuary, SouthCarolina.

Among the dominant species collected in the Great Bay –Little Egg Harbor estuary, within-year variation (amongmonths or sampling dates) accounted for the most variationin density. Variability in density within seasons could be ex-plained by variation in the timing of pulses of larvae enter-ing the estuary from distant spawning areas (Frank andLeggett 1983; Allen and Barker 1990). Estuarine spawnersshould therefore show less variability within a season thanthose from distant spawning areas. In this estuary, variationamong months was the highest for the estuarine-spawning

species (mean 63.2%), lowest for continental shelf spawners(mean 49.1%), and intermediate for continental shelf/estuarinespawners (mean 53.9%) (Table 2). The relationship betweenintrayear variability and spawning location may be compli-cated by estuarine- or inlet-specific processes that effect thespecific timing of spawning, hatching, or transport. The vari-ation in the timing of entry into the estuary from distantspawning grounds may be, in part, determined by local,episodic oceanographic events such as upwelling or down-welling that are known to occur at this site (Neuman 1996).

Allen and Barker (1990) found that the higher proportionof the variation in density of winter species compared withsummer was explained by interyear variation. In the presentstudy the few species that showed significant interyearvariability were both winter- (C. harengus) and summer-spawned species (G. ginsburgiand G. boleosoma). All fishthat showed significant interyear variability were near theextremes of their range and possibly their physiological lim-its (sensu Miller et al. 1991).

In summary, the seasonal pattern of estuarine larval fishassemblages in temperate east coast estuaries similar to ourstudy site may be driven by factors both internal and exter-nal to the estuary. For many species the timing, and perhapslocation, of spawning is set such that larvae that are trans-ported to estuarine nursery grounds are likely to encounterspecific environmental conditions that vary among species.The fishes that utilize the Great Bay – Little Egg Harbor es-tuary as nurseries are spawned in a variety of locations rang-ing from local estuarine spawning to very distant spawningin the Sargasso Sea. The geographic variation in spawninglocations also produces variation in the dependence upon lo-cal processes for dispersal during the larval period amongspecies (Suthers and Frank 1991). There is, however, evi-dence here that local processes do play a major role dictat-ing the timing of arrival of estuarine fishes, even those thatare spawned in areas far from the estuary. It is possible thatthe period during which larvae or any species are availablein the ocean is much longer in duration than the period overwhich conditions are favorable in the estuary. The windowof time during which survival is highest will therefore al-ways be occupied by larvae because larvae are available be-fore, during, and after the window is opened. We stress theneed to conduct long-term sampling programs that comparethe timing and abundance of larvae on the continental shelfwith the timing and abundance of larvae entering the estuaryto improve our understanding of the role of local estuarineconditions in regulating species assemblage patterns.

This work is the result, in part, of research sponsored byNOAA, Office of Sea Grant, Department of Commerce, un-der grant No. NA36-RG0505 (project R/F-65) and No.NA89AA-D-SG-057 (project R/F-42). Funding was also pro-vided by Institute of Marine and Coastal Sciences, RutgersUniversity, and the Electric Power Research Institute. J.F.Grassle, P.J. Morin, K. Wilson, M.J. Neuman, and two anon-ymous reviewers made helpful comments on earlier drafts.G. Gallagher and K. Stocks assisted with CNESS analysis.B. Zlotnik helped to assemble the manuscript. We thank thestaff of the Rutgers University Marine Field Station for over

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