American Fisheries Society Symposium 39:257–267, 2004© Copyright by the American Fisheries Society 2004

257

Sensitivity of Larval Fish Transport to Location,Timing, and Behavior Using a Particle Tracking Model

in Suisun Marsh, California

STEVEN D. CULBERSON*, CALLIE B. HARRISON, CHRISTOPHER ENRIGHT,AND MATTHEW L. NOBRIGA

California Department of Water Resources, 3251 S StreetSacramento, California 95816-7017, USA

Abstract.—We used a coupled hydrodynamic-particle tracking model to assess the rela-tive importance of factors affecting transport and entrainment risk for generalized fishlarvae in Suisun Marsh in the San Francisco Estuary, California. Factors examined in-cluded location of particle release, time of particle release, vertical migration behavior,and combinations thereof. Model sensitivity was evaluated by observing particle en-trainment into a water diversion in Suisun Marsh under various scenarios and com-pared to randomized input cases. Scenarios combining two or more factors indicatednonlinear interactions, including evidence of tidal pumping on neap tides near our testdiversion. Our study suggests that accurate modeling of larval transport depends onaccurate determination of fish locations, tidal and residual time scale circulation simula-tion, and fish behavior during early life stages. We also examined appropriate ap-proaches to modeling these factors.

* Corresponding author: sculbers@water.ca.gov

Introduction

Successful transport of eggs or larvae fromspawning to rearing habitats is critical to therecruitment success of many estuarine fishes(Dew and Hecht 1994; Barthem and Goulding1997; Moriyama et al. 1998). In the San Fran-cisco Estuary, California, eggs and early lifestages of several species, such as striped bassMorone saxatilis, longfin smelt Spirinchusthaleichthys , and threatened delta smeltHypomesus transpacificus, are spawned in fresh-water and are subsequently transported down-stream to brackish water nursery habitats(Moyle 2002). Although mechanisms are notwell understood, quantitative relationshipsbetween freshwater flow into the estuary andrecruitment of estuarine organisms have beendemonstrated (Jassby et al. 1995; Kimmerer2002). Numerous water diversions distributedthroughout the estuary can alter transport

flows and entrain young fish potentially im-pacting recruitment success (Arthur et al. 1996;Bennett and Moyle 1996).

Larval fish mortality is influenced by nu-merous, often interacting mechanisms (Houde1987; Bennett and Moyle 1996), making quan-titative descriptions of individual sources ofmortality difficult. The complexity of fish re-cruitment processes often necessitates model-ing approaches for understanding populationdynamics (Letcher et al. 1996). Here, we use acoupled hydrodynamic-particle trackingmodel to assess the sensitivity of location, tim-ing, and behavior on entrainment risk in anunscreened water diversion. Model scenarioswere based on generalized fish larvae duringspring (April–May), when native fishes typi-cally spawn in Suisun Marsh (Meng andMatern 2001).

We used the Delta Simulation Model-2Particle Tracking Model (PTM; CDWR 2002)as the analysis tool. Our goal was to assessthe sensitivity of early life stage fish transportassumptions to PTM input parameters, rather

258 CULBERSON ET AL.

than a quantitative assessment of entrainmentrisk for any particular species. As such, thisstudy is less concerned with specifying exactinitial release conditions and behavior thanwith developing an appropriate approach tomodeling fish larvae through analysis ofmodel sensitivity.

Sensitivity analysis allows prioritizationof model parameters, forcing functions, or sub-models to state variables of greatest importancein the model (Jorgensen and Bendoricchio2001). The process is inherently iterative andprovides the opportunity to conduct thoughtexperiments based on simulation responses.The description of simulation experimentsgiven here reflects how this approach guideduse of the PTM to address the following ques-tions of fish larvae entrainment risk in SuisunMarsh: (1) what are the relative influences oftiming, location of hatching (or transport intothe marsh), and vertical migration patterns onentrainment risk? (2) do combinations of thesefactors interact to influence entrainment risk?and (3) how is the PTM best used to model lar-val fish and diversion risk within the estuary?

Study Area

Our study focused on Suisun Marsh, where theCalifornia Department of Water Resources(CDWR) is required to characterize entrainmentrisk at several diversions (Figure 1). SuisunMarsh is the largest contiguous brackish marsh(~30,000 ha) on the West Coast of the UnitedStates. The marsh represents approximately12% of California’s remaining wetland habitatand is a significant nursery area for many fishspecies (Meng et al. 1994; Meng and Matern2001; Matern et al. 2002). The majority of SuisunMarsh (~22,000 ha) is managed for waterfowl.

There are 366 known water diversionsdistributed throughout Suisun Marsh (Herrenand Kawasaki 2001). Nearly 80% of the diver-sions operate via floodgates, and 2% arescreened to exclude fish. Typical intake diam-eters for Suisun Marsh floodgate diversionsare 0.9–1.2 m. In the present study, we simu-lated the influence of a single diversion, theMorrow Island Distribution System (MIDS;Figure 1). The MIDS fits the above descriptionof a typical Suisun Marsh water diversion andis being used in this study as a representative

diversion to evaluate influences on larval fishentrainment. We limit our analysis to this onediversion to clearly discern factors influenc-ing entrainment risk. The MIDS is located inGoodyear Slough, a 20–30-m-wide channelfringed with bulrushes Scirpus spp., cattailsTypha spp., and common reed Phragmites aus-tralis. The MIDS intake includes three 1.2-mgated culverts that are operated to allow wa-ter to enter the distribution system. The CDWRoperates and maintains MIDS jointly with theU.S. Bureau of Reclamation.

Methods

Delta Simulation Model-2Hydrodynamics and Particle TrackingModel

The primary modeling tool used for this studyis PTM (CDWR 2002). The PTM simulatestransport and fate of individual particlesmoving throughout Suisun Marsh and theSacramento–San Joaquin Delta. The modeluses velocity, flow, and stage output from aone-dimensional hydrodynamic model,DSM2 HYDRO (CDWR 2002). DSM2 HYDROwas adapted for the delta and marsh fromthe unsteady, open-channel flow USGSFourPt Model (DeLong et al. 1997). Time vary-ing boundary conditions for the hydrody-namic model include river and stream in-flows, State Water Project and Central ValleyProject water diversions, agricultural waterdiversions, irrigation and leach water drain-age flows, tide stage in Suisun Bay at Car-quinez Strait (Figure 1), and delta water con-trol operations. Fixed inputs include channeland flooded island geometry and roughnesscoefficients. The system geometry is modeledas a network of channel segments and open-water areas connected by junctions (Figure 1;additional information on the model is avail-able online at http://modeling.water.ca.gov/delta/models/dsm2/index.html).

The PTM uses the same system geometryas DSM2 HYDRO. The PTM was originallydeveloped as a two-dimensional model andsubsequently modified to a quasi-three-dimen-sional model (CDWR 2002). Particles are trans-ported within the network under the influenceof tidal flows and random mixing dynamics.

SENSITIVITY OF LARVAL FISH TRANSPORT TO LOCATION, TIMING, AND BEHAVIOR 259

FIGURE 1. Top: Location of the Sacramento–San Joaquin Delta and Suisun Marsh showing the MorrowIsland Distribution System (MIDS) and simulated release points. Bottom: Suisun Marsh portion ofDelta Simulation Model-2 Hydrodynamics and Particle Tracking model grid map showing graphicalrelationships between tidal channels and hydrologic junctions.

Deterministic and stochastic longitudinal,transverse, and vertical movements simulatethree-dimensional transport of neutrally buoy-

ant particles within channels. Longitudinalvelocity experienced by a particle is a functionof cross-section average velocity computed by

260 CULBERSON ET AL.

DSM2 HYDRO, modified by its cross-sectionposition relative to theoretical transverse andvertical velocity profiles. The theoretical trans-verse velocity profile accounts for lateral veloc-ity shear. Consequently, particles located nearshore move slower than those in mid-channel.The vertical velocity profile simulates shearalong the channel bottom resulting in a verti-cal transport gradient with faster particle trans-port near the surface (CDWR 2000). The DSM2PTM output data can be viewed as animationsthat display the movement of particles.

Model input parameters

Particle tracking scenarios were designed tosimulate fish larvae hatched upstream of themarsh during the simulation period or larvaehatched from adhesive delta smelt eggs depos-ited within the marsh. The study period (25April to 5 May 1996) was chosen to model (1)intermediate flow conditions, (2) a time of yearwhen larval stages of native fish were present(Meng and Matern 2001), and (3) a completespring-neap tidal cycle. Particle releases weremade over a full spring-neap cycle (25 April to9 May); model runs were extended through 15May to provide sufficient time for particles re-leased late in the series to disperse. In all sce-narios 10,000 individually tracked particleswere released to balance the potential bias dueto low numbers of simulated particles whileremaining computationally feasible.

Particle tracking scenario formulation

Particle release conditions (location and tidaltiming) were chosen to simulate appearanceof fish larvae in the system. Simulated behav-iors mimic vertical migration by larvae(Bennett et al. 2002). To evaluate the modeldependence on particle release conditions andbehaviors, a random base case was used forcomparison (model runs A–K; Table 1). Par-ticle release in the base case was random withrespect to location and timing, and particleswere simulated as neutrally buoyant.

Scenarios were generated by randomlyperturbing base case release conditions. Ran-dom variables (particle release location, date,time, and particle release number) for each sce-nario were generated using Microsoft Excel©.

Variability associated with random generationwas tested by repeating five scenarios fivetimes, each using different random inputs. Themaximum standard deviation of entrainmentwas 0.9%, indicating that the variability asso-ciated with random generation of a scenariowas relatively small.

We formulated scenarios in a heuristicfashion using results from initial runs to in-form subsequent experiments. Based on resultsof runs A–K, we generated runs L–S (Table 1),to investigate factor interactions. In runs L–S,particles were released in Goodyear Sloughwithin 4 km of the MIDS intake (same locationsas run B; see Figure 1) to amplify the effect onentrainment. Results were compared to run B;thus, run B served as base case for runs L–S.Runs L–O tested independent changes madeto run B, and runs P–S combined some of thesechanges to assess their interactive response.

A supplementary series of sensitivity runs(runs 1–15) was developed to focus on the in-fluence of tidal pumping observed using thePTM animation tool. The branched and loopednature of Suisun Marsh geometry interacts dif-ferentially with tidal strength to generate tidalpumping and trapping phenomena (Fischer etal. 1979). To investigate the effects of tidal pump-ing throughout the spring-neap cycle, particleswere released into Cordelia Slough (Figure 1)over 24-h periods for each day of the spring-neap cycle (25 April to 9 May). In particular,particle flux into Goodyear Slough was ob-served over time and associated with tidalamplitude, an indicator of tide strength.

We assume the percentage of particles di-verted into MIDS represents the likelihood ofentrainment. Cumulative particle entrainmentpercentages were used to compare the resultsof individual runs. The MIDS discharges wa-ter into Grizzly Bay (Figure 1). However, weassumed that all particles entering MIDS werelost (i.e., complete mortality). Thus, particlesthat entered MIDS were removed and did notsubsequently re-enter the diversion.

Results

The MIDS intake is flap-gated, allowing onlyunidirectional flow into the system. Maximumflow into MIDS varied slightly through thespring-neap cycle, but was about 6 m3/s,

SENSITIVITY OF LARVAL FISH TRANSPORT TO LOCATION, TIMING, AND BEHAVIOR 261

while peak flow in Goodyear Slough variedbetween 11.5 and 22.5 m3/s (Figure 2).

Depending on location, animations of in-dividual particle tracks showed that tidal time-scale excursions were between 2 and 5 km.This distance is comparable to characteristicchannel and slough lengths between distribu-tary confluences. As a consequence, rapid dis-persion from initial particle release locationswas observed in all scenarios.

Random marsh-wide particle release(base case)

Particles distributed randomly throughout themarsh exhibiting neutrally buoyant behavior

were unlikely to become entrained (2.6% of to-tal particles) in MIDS (Table 1). The model pre-dicted nearly half of the particles exited SuisunBay through Carquinez Strait (Figure 1) within3 weeks.

Influence of particle release location

Particle release location had the largest influ-ence on entrainment risk. Releasing particlesin a noncontinuous patchy distribution de-creased entrainment from 2.6 to 1.2% (Table 1;run A). By contrast, 37.5% of particles releasedwithin 4 km of MIDS were entrained (Table 1;run B). Conversely, particles released in theSacramento River near Rio Vista had almost

TABLE 1. List of Delta Simulation Model-2 Particle Tracking model runs. Entrainment is the percentageof released particles entrained at Morrow Island Distribution System. Entrainment values in parenthe-ses for runs A–K are the percent differences in entrainment relative to the completely randomized basecase scenario (2.6% entrainment). Entrainment values in parentheses for runs L–S are the percentdifferences in entrainment relative to run B, the proximate release base case scenario.

Run Release location Release timing Particle behavior Entrainment

A Patchya Random Neutrally buoyant 1.2 (–55)B Proximateb Random Neutrally buoyant 37.5 (1,342)C Remotec Random Neutrally buoyant 0.0 (–98)D Random Low tided Neutrally buoyant 3.6 (38)E Random High tidee Neutrally buoyant 1.8 (–33)F Random Night Neutrally buoyant 1.9 (–25)G Random Day Neutrally buoyant 2.9 (10)H Random Spring tidef Neutrally buoyant 2.3 (–13)I Random Neap tideg Neutrally buoyant 3.8 (47)J Random Random Reverse dielh 2.9 (11)K Random Random Dieli 3.4 (32)L Proximate Spring tide Neutrally buoyant 35.7 (–5)M Proximate Neap tide Neutrally buoyant 45.6 (22)N Proximate Random Reverse diel 35.5 (–5)O Proximate Random Diel 47.6 (27)P Proximate Spring tide Reverse diel 30.7 (–18)Q Proximate Spring tide Diel 44.7 (19)R Proximate Neap tide Reverse diel 45.2 (21)S Proximate Neap tide Diel 55.8 (49)a Particles released in Cordelia Slough, Hill Slough, and Little Honker Bay.b Particles released in Goodyear Slough within 3.7 km upstream or downstream of MIDS.c Particles released in Sacramento River at Rio Vista.d Particles released within +1 hour of peak low tide.e Particles released within +1 hour of peak low tide.f Particles released day before, during, and following full or new moon.g Particles released day before, during, and following quarter moon.h Particles move to upper 20% of water column by day and lower 20% by night.i Particles move to lower 20% of water column by day and upper 20% by night.

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no chance (<0.1%; Table 1; run C) of enteringMIDS.

Influence of particle release timing

Particles released during low tides initiallyexperience flood flows that drove them up-stream into the marsh. Flood tide releases re-sulted in longer residence times and in-creased entrainment (38% greater thanrandom base case; Table 1; run D). Conversely,particles released on ebb tides initially aremore likely to be flushed out of the marshthrough Carquinez Strait, thus decreasingentrainment (–33% from random base case;Table 1; run E).

Particles released during night experi-enced decreased entrainment, whereas dayrelease increased entrainment. (Table 1; runsF and G). Our study period included a tidalcycle when nearly all higher high tides oc-curred during night. The lowered entrainmentduring night release is likely due to the corre-lation between night and stronger ebb flows

in the marsh. In general, travel time to MIDSfor each of the forgoing scenarios (runs D, E, F,and G) is greater than a tidal cycle allowingextensive mixing prior to entrainment. Tidalmixing tends to minimize the entrainment dif-ference regardless of initial release conditions.

Particle transport response is sensitive tothe spring-neap time scale (Table 1; runs Hand I). Neap tide release increased entrain-ment by 47% compared to base, while springtide release reduced entrainment by 13%. Thisspring-neap tide influence was further inves-tigated in runs 1–15 (see below).

Influence of vertical migration behavior

Evaluation of particle behavior was limited tovertical migration patterns of diel (depth byday for 14 h, surface at night for 10 h) andreverse diel (depth at night, surface by day)movement of particles in the water column.Diel behavior increased entrainment by 32%whereas reverse diel behavior increased en-trainment by 11% (Table 1; runs J and K).

FIGURE 2. Modeled flows in Goodyear Slough and the Morrow Island Distribution System (MIDS).Also shown are corresponding moon phases.

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SENSITIVITY OF LARVAL FISH TRANSPORT TO LOCATION, TIMING, AND BEHAVIOR 263

Particle releases proximate to MIDS(proximate base case)

Influence of spring-neap tide on proximaterelease.—Spring tide releases proximate toMIDS resulted in 5% entrainment decreaserelative to run B, whereas neap tide releasesresulted in 22% entrainment increase (Table1; runs L and M). Spring versus neap tide re-leases proximate to MIDS exhibited the samediversion response pattern as the base case.However, the percentage change was less thanthe random case (spring random –13%; springproximate –5%; neap random 47%, neapproximate 22%; Table 1).

Influence of vertical migration behavior onproximate releases.—A 27% entrainment in-crease was observed when diel behavior (Table1; run O) was applied to the proximate releasecase. Reverse diel behavior resulted in a verysmall (5%; Table 1; run N) entrainment de-crease relative to the proximate release.

Combinations of spring and neap tides in com-bination with diel or reverse diel behavior on proxi-mate release.—The interaction of spring tiderelease with reverse diel behavior decreasedentrainment 18% (Table 1; run P), while springtide release with diel behavior increased en-trainment 19% (Table 1; run Q). Particle releaseduring neap tide coupled with reverse dielbehavior increased entrainment 21% (Table 1;run R). Neap tide release with diel behaviorresulted in the largest increase in entrainmentof any of the combined factors tested (49%;Table 1; run S). Combined effects were not ad-ditive of isolated singular effects indicatinginteractions among modeled particle behav-iors and initial conditions. For example, theinteraction of spring tide release with reversediel behavior decreased entrainment 18%(Table 1; run P), whereas the sum of singularspring tide release and reverse diel behaviorwas 10%.

Sequential release through the spring-neap cycle

Throughout the foregoing simulations, weobserved that particles were increasingly di-verted into Goodyear Slough during neaptides. However, based on tide strength alone,we anticipated that the greatest amount of

flow into Goodyear Slough would occur dur-ing spring tides. The apparent flux depen-dence on tidal strength suggests tidal or re-sidual flow asymmetry, generally referred toas tidal pumping (Fischer et al. 1979). To in-vestigate this further, we released particlesinto Cordelia Slough (Figure 1) for each day ofthe spring-neap cycle (25 April to 9 May; runs1–15). We observed the highest flux intoGoodyear Slough when particle release oc-curred during neap tide (25 April) and lowestflux during spring tide (4 May; Figure 2). Theincreased flow into Goodyear Slough duringneap tides increased water diverted into MIDS.However, overall particle entrainment intoMIDS remained relatively low (<4.5% of totalparticles released).

Discussion

The early life history of several estuarine fishspecies includes dispersal as eggs (stripedbass) or larvae (longfin smelt and delta smelt)from freshwater spawning grounds to lowsalinity nursery habitats (Moyle 2002). Withcontinued growth and larval development,these fishes increasingly exhibit tidal-diel be-havior combinations to maintain positionsalong the estuarine salinity gradient (Bennettet al. 2002). We believe that PTM results aremost relevant to passively drifting (dispers-ing) life stages. Furthermore, model resultsbecome increasingly less accurate as larvaegrow and develop fins and air bladders, struc-tures that improve their ability to maintainpositions in favorable habitats.

Acknowledging current model limita-tions, our results suggest the location of par-ticle release relative to water diversion posi-tion has an overwhelming influence onentrainment risk. This was our expectation,and it serves to validate model behavior. Par-ticles released far upstream of MIDS were al-most never entrained, whereas particles re-leased within 4 km of MIDS were entrainedan order of magnitude greater than base andall other single parameter scenarios. Distribu-tion of particle releases (patchy versus ran-dom) was important primarily because it al-tered the effective release location. Patchyrelease distribution resulted in decreased en-trainment, since all randomly released patches

264 CULBERSON ET AL.

happened to be located greater than 16 kmfrom MIDS. Notably, Herren and Kawasaki(2001) mapped 366 individual diversions dis-tributed throughout Suisun Marsh, demon-strating that nearly any marsh location is neara diversion. Presently, PTM has not been con-figured to include all Suisun Marsh diver-sions. It is unclear whether collectively, thesediversions represent a 366-fold increase overmodeled conditions in entrainment risk, or ifcollectively, entrainment within the marsh isthe result of a combination of factors (e.g., re-gional and local tidal conditions, diversioncharacteristics) that results in less than 366-fold increased entrainment risk, as we havesuggested. Furthermore, we were unable todetermine whether collectively 366 diversionswithin Suisun Marsh have impacts to fishesat the population level.

Semidiurnal (low or high tides) or diur-nal (night or day) release timing affected en-trainment. We assumed that diel behaviorplaced particles near channel bottom for 14 h(daylight) and near surface for 10 h (dark). Thesimulated quasi-three-dimensional advectionand dispersion of particles caused greater re-tention of near bottom particles due to reducednear bottom water velocities (CDWR 2002).Greater retention likely increased entrainmentpotential in diel model runs by increasing resi-dence times near the diversion. Significantly,reverse diel releases, distributed both proxi-mately and randomly, resulted in opposingtrends. This suggests that water column ori-entation relates nonlinearly to other factors ofinterest, especially tides. Further work to de-termine the relationship between marsh fishspawning episodes and tidal sequence is ofparamount importance. If, however, the spawntiming is not known, spring-neap biasingshould be examined by selecting a release pe-riod lasting at least one spring-neap cycle. Theeffect of day versus night release timing wasconfounded by tidal covariation during ourstudy. Thus, apparent day–night differenceswere partly an artifact of our chosen time pe-riod. Investigators need to be aware of poten-tial tidal-diel aliasing when designing particletracking studies or interpreting results. Impli-cations for predation aside, we believe thatgiven a large number of simulation trials thatspanned the range of tidal/diel time scale in-

teractions, differential transport of fishspawned during day or night will likely beexplained better by covariance with tidalstrength and behavior assumptions.

Decreased tidal strength during neaptides increased particle entrainment due toincreased residence time near MIDS. In ourexperience, tidal excursions in Suisun Marshare comparable to characteristic channel bi-furcation lengths allowing rapid tidal disper-sion. Therefore, we hypothesize that intensi-fied tidal mixing during spring tides (Staceyet al. 1999) is more likely to transport particlesout of Suisun Marsh within the fortnight timescale. Consequently, there were fewer oppor-tunities for particles to encounter MIDS. Par-ticles released near MIDS were more likely tobe entrained (1–2 d). As a result, these par-ticles have less time to experience tidal mix-ing and are more affected by release timing.The influence of spring-neap release timingin a multiple diversion scenario remains un-known and should be explored after allSuisun Marsh diversions have been incorpo-rated into the PTM.

The imbalance between spring (–13%)and neap tide entrainment (+47%) suggeststhat mechanisms other than fortnightly vary-ing tidal strength and tidal excursions influ-ence particle movement. Based on tide strengthalone, we expected a priori that the largestflow volumes into Goodyear Slough wouldoccur during spring tides. We found the op-posite to be true, suggesting the influence oftidal asymmetry as the flux-forcing mecha-nism. Channel and slough networks createopportunities for tidal waves to reach the samelocation by different path lengths. Tidal pat-terns on the spring-neap time scale can gener-ate nonlinear interactions between tide stageand channel geometry at the tidal time scale.Particular tide strength and channel lengthcombinations can interact nonlinearly to tid-ally pump water (Fischer et al. 1979). Resultsof sequential releases through the spring-neapcycle demonstrated this nonlinear interaction(Table 1; runs 1–15; Figure 3). Tidal pumpingincreased flow into Goodyear Slough duringneap tides, incrementally increasing entrain-ment into MIDS. Complex geometry of SuisunMarsh and the Sacramento–San Joaquin Deltalikely creates many interrelated tidal pumps

SENSITIVITY OF LARVAL FISH TRANSPORT TO LOCATION, TIMING, AND BEHAVIOR 265

Goodyear Slough tide stage

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FIGURE 3. Cumulative particle flux into the Morrow Island Distribution System for a series of runswith indicated release dates. Upper pannel displays tide stage information for Goodyear Slough overthe same time period.

266 CULBERSON ET AL.

that influence hydrodynamics in the San Fran-cisco Estuary. Obtaining a better understand-ing of tidal pumping could greatly enhancethe ability to predict estuarine transport pro-cesses for physical (e.g., salt, contaminants)and biological (e.g., fish larvae) variables ofmanagement interest.

The PTM produced nonadditive outputswhen combinations of independent factorswere modeled simultaneously. Although de-rived from a limited set of combinations, thisresult suggests nonlinear interaction of tidalpattern and particle behavior. Presently, thePTM is not capable of modeling complex fishbehaviors that may influence entrainmentrisk. For instance, depending on hydrologicconditions, larval fish in the San FranciscoEstuary have been shown to switch betweenvertical and horizontal migrations to main-tain position within favorable habitats(Bennett et al. 2002). Further understandingof fish behaviors and continued model devel-opment can improve the applicability of thesePTM results to greater segments of fish lifehistory and to a wider array of species.

Conclusions

The most important result of this study is thatdiversion of larval fishes largely depends ontheir proximity to the diversion location.Within this general conclusion, several moresubtle results emerged. First, spring-neap time-scale tidal strength differences and diel behav-ior have a significant impact on particle resi-dence time and entrainment potential. Second,nonlinear interactions of tidal strength andsystem geometry tidally pump water, thus fur-ther increasing entrainment during neap tides.Sloughs converging at the head of GoodyearSlough apparently pump water more readilyinto Goodyear Slough during neap tides. Webelieve that this system characteristic mayhave important management implications forprotecting larval fish.

We also underscore the need for modelsthat are able to correctly simulate tidal move-ment over a range of time and space scales—attributes that the PTM has been demonstratedto possess. Nonlinear interactions betweentidal strength and system geometry requirewave propagation to be simulated with accu-

rate phasing and stage amplitudes. Accuratemeasurement of system bathymetry and ge-ometry and translation to model input are alsorequired to produce a locally accurate and re-gionally effective modeling tool. The neap tidepumping observed in Goodyear Slough is asubtle consequence of tidal strength, and phas-ing outputs of the model for nearby regionsshould be verified by field measurements.

Heuristic application of this mechanistichydrodynamic and particle tracking model isuseful for sharpening study questions anddiscovering exceptions to general conclusions.By conducting the modeling exercises as acollaborative analysis between fish biologistsand hydrodynamicists, we were able to gen-erate hypotheses about local and regional fishsurvival related to interactions of local andregional tides, hydraulic geometries, and lar-val fish responses to these variable environ-mental conditions. Feedback between particletracking model applications and larval fishfield data collection will improve the capabili-ties of particle tracking models and informmonitoring programs investigating early lifestages of fish.

Acknowledgments

We thank A. Miller and B. Tom for assistancewith DSM2 PTM. Comments from R. Brownand two anonymous reviewers improved themanuscript.

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