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

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<ul><li><p>Larval fishes of a Middle Atlantic Bight estuary:assemblage structure and temporal stability</p><p>David A. Witting, Kenneth W. Able, and Michael P. Fahay</p><p>Abstract: We collected weekly, quantitative ichthyoplankton samples over 6 years (19891994, 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.</p><p>Rsum: Nous avons prlev hebdomadairement des chantillons quantitatifs dichtyoplancton durant 6 ans(19891994, 1 309 chantillons) pour tablir les chelles temporelles de variabilit de labondance et de la prsence degroupements de larves de poissons prs du bras Little Egg dans le sud du New Jersey, aux tats-Unis. Nous avonsrecueilli des espces qui frayent dans lestuaire (30%), la fois dans lestuaire et sur la plate-forme continentale(35%), sur la plate-forme continentale seulement (25%) et dans la mer des Sargasses (10%). Les rsultats suivantslaissent penser quil y a une progression saisonnire rpte annuellement des groupements despces : (i) lesabondances relatives des 20 espces dominantes nont pas chang significativement danne en anne, (ii ) la variationde la densit de lespce dominante sexpliquait principalement par une variation intra-annuelle plutt quinter-annuelleet (iii ) lanalyse multivarie de la matrice des groupements a permis de reprer cinq groupements saisonniers quitaient prsents durant les six annes. Nous avons observ que le moment de formation et la dure de chacun de cesgroupements saisonniers taient corrls avec deux caractristiques du cycle annuel de la temprature, soit le degr detemprature (temprature basse ou leve) et la tendance de la temprature (temprature croissante ou dcroissante).Nous pensons que la survenue rpte des groupements de larves de poissons dans les estuaires temprs de la cteamricaine peut en partie tre dtermine par des processus environnementaux locaux.</p><p>[Traduit par la Rdaction] Witting et al. 230</p><p>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</p><p>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.</p><p>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</p><p>Can. J. Fish. Aquat. Sci.56: 222230 (1999) 1999 NRC Canada</p><p>222</p><p>Received April 27, 1998. Accepted October 8, 1998.J14559</p><p>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.</p><p>1Author to whom all correspondence should be addressed.e-mail: david.witting@noaa.gov</p><p>I:\cjfas\cjfas56\Cjfas-02\F98-175.vpThursday, March 04, 1999 3:29:12 PM</p><p>Color profile: DisabledComposite Default screen</p></li><li><p>vary dramatically from year to year, we can infer that larvalsupply is a source of interannual variation in the seasonalprogression of species.</p><p>Study areaThe Great Bay Little Egg Harbor estuarine system (Fig. 1) is</p><p>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 28C) 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).</p><p>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.</p><p>Sampling protocolWe collected samples weekly by suspending a 1-m-diameter</p><p>(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</p><p>(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).</p><p>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.</p><p>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.</p><p>Data analysisWe used nested ANOVA (Hicks 1993) to partition the temporal</p><p>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.</p><p>We conducted analyses that tested for temporal consistency in</p><p> 1999 NRC Canada</p><p>Witting et al. 223</p><p>Fig. 1. Great Bay Little Egg Harbor estuary in southern NewJersey with the sampling location at Little Sheepshead Creeknear Little Egg Inlet.</p><p>I:\cjfas\cjfas56\Cjfas-02\F98-175.vpThursday, March 04, 1999 3:29:16 PM</p><p>Color profile: DisabledComposite Default screen</p></li><li><p> 1999 NRC Canada</p><p>224 Can. J. Fish. Aquat. Sci. Vol. 56, 1999</p><p>SpeciesTotalno.</p><p>Cumulative%</p><p>Spawningarea</p><p>No.staged</p><p>Preflexion(%)</p><p>Flexion(%)</p><p>Postflexion(%)</p><p>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</p><p>Note: Spawning areas are estuaries (E), continental shelf (C), or Sargasso Sea (SS) based upon Able and Fahay (1998).</p><p>Table 1. Sample size and flexion stage for the 20 most abundant forms collected from Little Sheepshead Creek inthe vicinity of Little Egg Inlet.</p><p>Species R2Date within monthand year (%)</p><p>Month withinyear (%)</p><p>Amongyears (%)</p><p>Monthrange</p><p>Averagedensity</p><p>Interyear(CV)</p><p>Anchoa mitchilli 0.75*** 32.5*** 62.4*** 5.2 ns 611 216.730 78.10Syngnathus fuscus 0.76*** 28.3*** 71.7*** </p></li><li><p>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.</p><p>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 Orlocis (1978) chord distance and Grassle andSmiths (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</p><p>HN N CmN Cm</p><p>ik mi ik</p><p>i|</p><p>[( ) ]( )</p><p>= 1</p><p>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</p><p>[ , , ]!</p><p>[ !( )!]N C m</p><p>Nm N m</p><p>ii</p><p>i</p><p>=</p><p>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.</p><p>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</p><p> 1999 NRC Canada</p><p>Witting et al. 225</p><p>Fig....</p></li></ul>

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