Reconstructing migrations of individual cod(Gadus morhua L.) in the Baltic Sea by usingelectronic data storage tags

STEFAN NEUENFELDT,1,* HANS-HARALDHINRICHSEN,2 ANDERS NIELSEN1 ANDKEN H. ANDERSEN1

1Danish Institute for Fisheries Research, Technical University ofDenmark, Charlottenlund Castle, DK-2920 Charlottenlund,Denmark2Leibniz-Institute of Marine Sciences, Dusternbrooker Weg 20,D-24105 Kiel, Germany

ABSTRACT

A new methodology is presented to reconstructmigration pathways of individual fish inhabiting eco-systems with moderate-to-strong gradients in tem-perature or salinity. The method uses measurements ofambient pressure, temperature and salinity obtainedfrom electronic data storage tags attached to individ-ual fish and is particularly applicable in areas withnegligible tides. We demonstrate the method withBaltic cod. Hydrographic fields obtained from hydro-dynamic modelling were used as a geolocation data-base to identify daily positions of Baltic cod bycomparison with the environmental data collected bythe tags. Using randomly distributed individualparameter perturbations in the range of the instrumentprecision of the tag we simulated a cod migratingthrough the Baltic Sea. The distance between theprescribed and geolocated positions of this artificialcod was on average 2.9 ± 4.7 (SD) km. Subsequently,the method was used to reconstruct migration routes of10 real cod tagged in the Bornholm Basin of the BalticSea in early spring 2003. Differences were comparedbetween the tag data and the geolocation database.The uncertainty in geolocation at recapture day wason average 75 ± 23 (SD) km, as shown by comparisonbetween geolocated position and recapture position.

Key words: Baltic Sea, cod migrations, data storagetags, geolocation

INTRODUCTION

Reconstructing individual fish migration tracksenabled new insights into fish behaviour, for examplemulti-year homing of Atlantic cod to a spawningground at the Newfoundland coast (Robichaud andRose, 2001), timing of North Sea cod migrations(Turner et al., 2002), or selective tidal transport ofNorth Sea demersal fish (Arnold and Holford, 1995).In geolocation, environmental information obtainedby electronic data storage tags from recaptured fish isused to estimate the geographical position of indivi-dual fish throughout the time they were at liberty (e.g.Block et al., 2001; Teo et al., 2004). Basically, move-ments of individuals are related to gradients of envi-ronmental parameters like temperature or day-lengthand spatially heterogeneous features such as bottomdepth and tidal patterns (Hill, 1994; Hunter et al.,2003). The precision of estimates of geographic posi-tion can be improved by combining more than onevariable in the analysis (Block et al., 2001).

Here we present a new geolocation method,designed to gain knowledge on the timing and longi-tudinal direction of adult cod migrations in the BalticSea. The method is potentially applicable in otherregions with similar hydrographic characteristics suchas the Gulf of St Lawrence, the Black Sea or fjords.

The Baltic Sea (Fig. 1) is a semi-enclosed brackishwater system consisting of several basins. Tidal motionin the Baltic Sea is insignificant and does not enablethe location of demersal individuals by the tidallocation method (Hunter et al., 2003). The basins aretoo large for using acoustic telemetry (Simpfendorferet al., 2002; Ehrenberg and Steig, 2003) which oper-ates on a much smaller spatial scale. Temperature,salinity and oxygen content are vertically stratified onthe scale of meters. A permanent halocline separateslow-salinity surface water from high-salinity deepwater. Below the halocline, temperature and salinity ata given depth differ by basin (Fennel, 1995), andinside a basin salinity varies horizontally by approxi-mately 1 on the scale of tens of kilometres. Themethodology we present here is based on combineddata of pressure, temperature and salinity obtained by

*Correspondence. e-mail: stn@difres.dk

Received 1 November 2005

Revised version accepted 7 January 2007

FISHERIES OCEANOGRAPHY Fish. Oceanogr. 16:6, 526–535, 2007

526 doi:10.1111/j.1365-2419.2007.00458.x � 2007 The Authors.

tags attached to adult cod and hydrographic propertyfields obtained from hydrodynamic modelling. Themodel output is used as a geolocation database toidentify individual pathways of Baltic cod by com-parison with environmental data collected by each tag.

Cod in the eastern Baltic Sea spawn in the Born-holm Basin (Fig. 1). Commercial cod fishing has beenbanned during the summer months in this area since1995 to allow undisturbed spawning (ICES 2004).This measure aims at rebuilding the cod populationwhich is now at a low level (ICES 2006). Timing andposition of the closed area is based on extensive trawlsurveys (Wieland et al., 2000); however, factorsaffecting the timing and spawning site fidelity ofindividual cod are not known. For example, cod whichare assumed to spawn in the Bornholm basin couldspawn in the Western Baltic, if there is anoxia in thedeep layers of the Bornholm basin.

The current study has two main aims. Firstly, wedemonstrated the utilization of our geolocationmethod by modelling the environmental experience ofa simulated cod track. Secondly, the migration tracksof real cod tagged in the Bornholm Basin of the BalticSea (Fig. 1) in early spring 2003 are presented. Thedifferences between the measured ambient environ-ment of the fish and the geolocation database wereanalysed and the uncertainties in position estimatesdue to the present measurement technology andhydrographic database are discussed.

MATERIAL AND METHODS

Tagging

Baltic cod >45 cm were caught on a long line in theBornholm Basin at 30–40 m bottom depth and raisedslowly to the surface (ascent time was about 15 min)in order to avoid damage to the swim bladder. Aftercapture, the cod were transferred to a 0.75 m3 tank.Those cod that were swimming at the tank bottomafter 1–6 h were tagged. Individual cod were anaes-thetized using MS-222 (Sandoz, Basel, Switzerland;dosage 1 : 20000). Total length (LT) was measured,then the cod were placed in a U-shaped plastic cradle(90 cm long, 15 cm wide and 7.5 cm deep), with a wettowel placed over head and operculae. A 20-cm pianowire was attached at each end of the tag. The two wireends were passed through a 1 · 5 · 0.2 cm siliconepad, and were then carefully passed through the fishesdorsal musculature approximately 2 cm below the firstdorsal fin, by using two injection needles. The wireswere passed through a similar silicone pad and a poly-ethylene disc on the other side, thereby protecting thefish’s skin and keeping constant distance between thewire ends. The wires were then twisted approximately0.5 cm above the poly-ethylene disc, to allow forgrowth of the fish. Excess wire was removed. Thewhole tagging procedure took a maximum of 2 min perfish before the fish were gently released from thestationary vessel. Star-Oddi CTD (Conductivity,

Figure 1. The Baltic Sea with the Bornholm basin (rectangle) shown as a surface plot. (a) Kattegat, (b) the western Baltic,(c) the Arkona Basin and (d) the Stolpe Trench. The cross (+) in the surface plot indicates the tagging area.

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Temperature and Depth) tags were used. Each tag wasprogrammed to record pressure, ambient temperatureand conductivity once every hour for a maximumrecording period of 2 yrs. The tags converted theconductivity measurements into salinities automatic-ally. After calibration with known salinities, theconductivity-based tag measurements of salinity wereaccurate to ±0.5. Temperature measurements aftercalibration were accurate to ±0.1�C, and pressure wasmeasured accurate to ±0.2 dbar.

Hydrodynamic model

For the reconstruction of migration routes of individ-ual cod, a comprehensive geolocation database con-taining the spatial and temporal development of therelevant hydrographic conditions in the Baltic Sea wascreated. A hydrodynamic model, based on the freesurface Bryan-Cox-Semtner model (Killworth et al.,1991), which is a special version of the Cox numericalocean general circulation model (Bryan, 1969; Semt-ner, 1974; Cox, 1984), was used to generate tem-perature, salinity and oxygen conditions and variationsin time and space. A detailed description of theequations and modifications made to adapt the modelto the Baltic Sea can be found in Lehmann (1995) andLehmann and Hinrichsen (2000a). Physical propertiessimulated by the hydrodynamic model agree well withknown circulation features and observed physicalconditions in the Baltic (for further description seeLehmann, 1995; Hinrichsen et al., 1997; Lehmannand Hinrichsen, 2000a). The model domain comprisesthe entire Baltic Sea (Fig. 1) including the Gulf ofBothnia, Gulf of Finland, Gulf of Riga as well as theBelt Sea, Kattegat and Skagerrak. The horizontalresolution is 5 km, with 60 vertical levels specified.The thickness of the different levels is chosen to bestaccount for the different depth minima betweenadjacent basins in the Baltic.

The Baltic Sea model is driven by atmospheric dataprovided by the Swedish Meteorological and Hydro-logical Institute (SMHI, Norrkoping, Sweden) andriver runoff taken from a mean runoff database(Bergstrom and Carlsson, 1994). The meteorologicaldatabase covers the whole Baltic Sea drainage basinwith a grid of 1� lat. · 1� long. squares. Meteorologi-cal parameters, such as geostrophic wind, 2-m airtemperature, 2-m relative humidity, surface pressure,cloudiness and precipitation are stored with a temporalincrement of 3 h.

Prognostic variables of the hydrodynamic model arethe baroclinic current field, the three-dimensionaltemperature, salinity and oxygen distributions, thetwo-dimensional surface elevations and barotropic

transport. These prognostic variables were extractedfrom the model every 24 h, and formed the geoloca-tion database for the subsequent analysis.

Longer circulation model runs resulted in increas-ing uncertainties in the simulated physical propertyfields, and hence in the estimated geolocation of tag-ged individual fish. These uncertainties can arise fromerrors in the specified forcing, boundary and initialconditions, and sub grid-scale processes not resolvedby the model dynamics. To overcome at least some ofthese problems the utilization of assimilation tech-niques was extremely important for long-term simu-lations. As initial conditions, the three-dimensionaldistributions of temperature, salinity and oxygenconcentrations were taken from a hydrographic surveyof Baltic cod covering different sub-areas of the BalticSea undertaken prior to the tagging period (15 March2003). During the survey the hydrographic propertydistributions in the Kattegat, the western Baltic, theArkona Basin, the Bornholm Basin and the StolpeTrench (Fig. 1) were recorded.

Prerequisites for data assimilation as applied hereare hydrographic field measurements on an approxi-mately regular grid. The observations must be hori-zontally distributed to ensure the smooth merging ofobservations with model data at the edges of thehydrographic field. It was also assumed that the fieldobservations taken during one hydrographic surveywere quasi-synoptic. Horizontal maps were constructedby interpolating the physical property data onto themodel grid using objective analysis (Bretherton et al.,1976). An isotropic covariance function

fðrÞ ¼ r2 expð�r2=R2Þ ð1Þwas used with R = 25 km, r2 the variance and r thedistance between data points. It was assumed that theerror variance due to measurement errors and small-scale noise amounted to 15% of the total variance ofthe fields, because our field data showed that errorvariance did not exceed 15% of the total variance. Forthose areas where the expected root mean square(r.m.s.) error was £50%, observations substitutedmodel data on the model grid; for those areas wherethe r.m.s. error was >50% the following function wasused to merge observations with model data.

Zmðx; yÞ ¼ Zmodðx; yÞ � a½Zmodðx; yÞ � Zobsðx; yÞ� ð2Þ

With

a ¼ 1; r.m.s. � 50%

e�r:m:s:�50

12:5ð Þ2 ; r.m.s. > 50%ð3Þ

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Zm represents the merged temperature and salinityfields, Zmod the model data prior to the assimilation,and Zobs are the field observations. Data outside thesurveyed area were taken from a previous model runrepresenting the seasonal hydrographic conditionsprior to the observational period. This model run wasbased on climatological databases (mean seasonaldevelopment of hydrographic parameters) and was notupdated by data assimilation. Re-initializations of thephysical property fields have been performed for April,May, July, August and November 2003 utilizing datafrom hydrographic cruises.

Geolocation

Starting from the known fish release location, for every24-h time step the geographic position of each fish fromrelease to recapture was determined by comparing thetag-measured temperatures and salinities at depth withthe environmental variables as obtained from thehydrodynamic model (geolocation database). Themethod requires all available parameters, ptag, Ttag, andStag (representing the tag measured pressure, tempera-ture and salinity data) to estimate the daily geograph-ical positions of individual fish. The positions wereestimated by minimizing the residuals between tag dataand model output (pmod, Tmod, Smod) with respect toposition. The residual error � is calculated as:

e ¼ w1ðptag � pmodÞ2 þ w2ðTtag � TmodÞ2

þ w3ðStag � SmodÞ2 ð4Þ

Weights are allocated to the various parametersaccording to environmental variability and instrumentprecision; for each parameter i, the correspondingweight wi is determined as the ratio between theinstrument precision and the variance of the para-meter found in the field. Having considered the rangeof environmental variability for the Bornholm Basin

the weighting factors were, for pressure w1 = 0.00073,for temperature w2 = 0.0025 and for salinityw3 = 0.00063. Weighting factors are necessary inorder to make parameters of incommensurable unitscomparable, by ensuring that information obtainedfrom each parameter is scaled to the parameter range.

Geolocation was performed on a daily basis, usingtag data and model data from midnight. We assumed amaximum travelling speed, utravel, of 20 km day)1,corresponding to a maximum average swimming speedof 0.5 body length per second when averaged over24 h. This assumption was implemented by limitingthe available positions during a 24-h time step to acircle with 20 km radius, centred on the actual posi-tion of the fish.

Furthermore, it was assumed that the remainingdistance to the known recapture location was always<utravel tremain, the time left to recapture. Thisassumption was implemented by limiting the availablepositions during a 24-h time step in addition to a circlewith radius d2 = d1tremain, centred on the recaptureposition of the fish. The available positions during thenext time step were then restricted to the intersec-tional area of the two circles.

Model validation

To demonstrate how geolocation of Baltic cod workswhen the environmental data from the model areconsidered correct with small-scale noise only, anartificial cod track was simulated, with a migrationstarting in spring in the vicinity of Gedser (Fig. 2).Recapture was assumed to take place 35 days later inthe Gdansk Deep, approximately 500 km east of thestart location. The migration route passed through theArkona Basin, the Strait of Bornholm into theBornholm Basin, where the fish performed an anti-cyclonic loop within the centre area. In the vicinityof 55�30¢N, 15�30¢E the artificial cod remained

Figure 2. Simulated track (thick line) and estimated track (thin line) of an artificial cod.

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stationary for a time period of 10 days. After leavingthe deep central area of the Bornholm Basin it swamto the Gdansk Deep where recapture took place onMay 5th. Along its migration path, the simulated codrecorded its ambient conditions. These environmentaldata were taken for April conditions from 4-yr aver-ages of the six hourly resolved model fields (Lehmannand Hinrichsen, 2000b). The geolocation methodthen recalculated the migration route of the cod byutilization of Equation 4, and by taking into accountthe prescribed maximum swimming speed as a furtherconstraint. In order to account for measurement errorsand small-scale noise of the environmental data, weused randomly distributed individual parameter pert-urbations in the range of the instrument precision ofthe tag.

Finally, analyses of the accuracy of geolocationpositions with respect to measured and simulatedhydrographic parameters were performed, and a sen-sitivity study on the influence of fish swimming speedon the uncertainty of geolocated positions of taggedfish was carried out.

RESULTS

Model validation

The prescribed and the geolocated migration routesof the artificial cod were closely matched (Fig. 2).The distance between the prescribed and geolo-cated positions of the cod was on average 2.9 ± 4.7(SD) km.

The survey hydrographic data were used to test theperformance of the hydrodynamic model. The modelerror increased for longer model runs without assim-ilating survey data. The maximum deviations of themodel from the observed environmental conditionsduring the hydrographic survey after approximately

1 month of modelled hydrographic development wereabout 1–2 in salinity (in the halocline) and 1�C intemperature (in the thermocline). Note that the hal-ocline in this area of the Baltic Sea has a strong ver-tical gradient (�1 m)1). Thus, with respect to themodel’s vertical resolution (3 m), this might lead tosmall uncertainties in the determination of the halo-cline depth.

Between the modelled hydrographic properties afterdata assimilation and the hydrographic propertiesobserved during the surveys, the probability densityfunctions of errors at 80% bottom depth indicate anaverage model error of )0.9 ± 1.5 (SD) for salinityand 0.1 ± 1.6 (SD) �C for temperature.

Geolocation

When cod migrated through the Bornholm Basin,they experienced changing temperature and salinityconditions at different depths (Fig. 3). The taggedcod experienced a change in depth from about 35 to75 m between Julian days 150 and 170 (from 31 to51 days after release in the Bornholm Basin; Fig. 3).The change in depth was accompanied by anincrease in salinity from 7.5 to approximately 17,and a decrease in temperature from approximately 5to 3�C.

Geolocated tracks from nine recaptured cod showthat individual migrations were 54–126 days (Fig. 4).The data from the tagged cod in Fig. 3 have beentransformed to the track shown in Fig. 4, Panel I. Twocod moved from the tagging location on the east coastto the west coast of Bornholm (Fig. 4a,e), but returnedand were recaptured close to the tagging location.Three other cod (Fig. 4f,h,i) visited Stolpe Trench(Fig. 1). Six out of the nine cod were recaptured closeto the release point. Three of the nine cod visiteddepths >80 m. The salinity data indicate that these

Figure 3. Tag records of depth (solidblack), salinity (dotted), and tempera-ture (grey dashed) for a tagged codrelease on 28 April 2003 in the Born-holm Basin of the Baltic Sea.

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fish visited the narrow region (Fig. 1) in the centre ofthe Bornholm basin.

Residuals obtained between physical parametersmeasured by the tags, and the corresponding valuesgenerated by the model were used for quality control.The histograms for all the parameters taken intoconsideration indicate that for all parameters (pres-sure, temperature and salinity) 66% of the residualswere in the range of 0–0.5 (Fig. 5).

The recapture position and the geolocated positionon the day of recapture can be compared in order togain an estimate of geolocation accuracy. With noforcing towards the recapture position on the day ofrecapture, the distance between the geolocated posi-tions and recapture positions was on average72 ± 23 km. However, the recapture positions could

be relatively uncertain due to misreporting, andrecapture for most fishes took place in the mixed bodyof water above the halocline. Within the latter, geo-location was difficult to perform because of the weakhorizontal and vertical temperature and salinity gra-dients.

The residuals between the tag data and the modeloutput can be decreased by using a faster travellingspeed. However, this decline was observed only up to atravelling speed of 35–40 km day)1 (Fig. 6). Althoughthe residuals between tag measurements and modeldata decreased by 25%, when the maximal travellingspeed was doubled from 20 to 40 km day)1, the geo-location was not necessarily more accurate. At thetime of recapture, the average distance betweengeolocated positions and recapture positions at a

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Figure 4. (a–i) Estimated tracks from recaptured cod. Black dots indicate the recapture positions. See text for details.

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travelling speed of 40 km day)1 (without forcing thetrack towards the recapture position) was 79 ± 57 km,very similar to the value determined at 20 km day)1.The standard deviation, in contrast, increased inproportion to the increase in travelling speed.

One cod (shown in Fig. 7) crossed the boundariesbetween adjacent basins of the Baltic Sea, and wasrecaptured after 295 days at 55.06�N and 11.07�E

(Fig. 7). The cod spent 197 days in the Bornholmbasin in the low salinity that is characteristic for waterdepths <50 m.

DISCUSSION

Depth, salinity and temperature records from indi-vidual tags indicated whether cod were stationary or

(a)

(c)

(b)

Figure 5. Frequency distributions of the residuals (tag –model) from the geolocation: (a) pressure, (b) salinity and(c) temperature.

Figure 6. Standardized residuals (� in equation 4 standard-ized to 1 at utravel = 10 km day)1) dependent on the max-imal travelling speed of 40 km day)1 corresponding to amaximum sustained swimming speed of approximately1 bl s)1.

Figure 7. Estimated track from a recaptured cod, showingthat the individual left the Bornholm Basin and went intothe Western Baltic Sea. The black dot indicates the recap-ture position.

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moving, and whether fish migrated into the deepcentre of a Baltic basin. Moreover, the quality ofmeasurements recorded by tags, coupled with theprecision of the hydrographic model data, permittedprecise reconstruction of individual fish tracks when-ever fish moved through areas with a unique combi-nation of salinity, temperature and depth. Geolocationin areas where the hydrography was not unique wasmore problematic. However, the reliable sections oftrack (including fish release and recapture points)could be used in combination with assumptions aboutfish swim speeds to reasonably reconstruct theremaining portions of each movement track.

Within the mixed-surface layer, tag measurementand model errors corresponded on average to a geolo-cation uncertainty of 75 km, as shown by comparisonbetween geolocated position and recapture position.This is less error than that observed for light-basedgeolocations. The error in estimating latitude based onlight measurements is approximately 140 km (Welchand Eveson, 1999). Better light-based position esti-mates are possible, but they depend crucially on areliable estimate of the relation between light anddepth (Welch and Eveson, 2001). This relation is dif-ficult to assess for many free ranging marine species,because in most cases water clarity varies not only withdepth but also with time and position due to patchinessin cloud coverage and plankton concentration. Hence,to improve light-based geolocations, the positionwould have to be known before the relation betweenlight and depth can be estimated. Another advantageof our method over light-based geolocation is thatcertain portions of track can have salinity and depthsignatures which force the fish into relatively smallparts of the study area, thus reducing error associatedwith part of the track. Specifically, certain areas withinand below the halocline waters of the Bornholm Basinand generally within a Baltic Sea basin have uniquesalinity and depth characteristics. For example, thetracks downloaded from three out of nine tags indica-ted that the cod experienced depths greater than 80 m.In the study zone, there is very limited area representedby this depth range (detailed map in Fig. 1). Moreover,it was known that the fish were in the Bornholm Basin,as the recorded salinities are known to occur at thatdepth exclusively in that basin. As a result, they wereknown to have passed through the centre of theBornholm Basin. Reliable geolocations such as thesecan be used in combination with the tagging andrecapture positions to improve the method by forcingthe tracks through these locations.

Increasing the maximum swimming speed did notdecrease geolocation uncertainty. When the fish were

not forced to return to their recapture positions, theaverage distances between geolocated position and thereal recapture location on the last day of liberty werealmost equal, independent of whether the fish wereallowed to swim 20 or 40 km day)1. The maximumswimming speed of 20 km day)1 is in line with lit-erature values. Atlantic cod on the north-east New-foundland shelf have been reported to migrate 7 to24 km daily, corresponding to 0.23 to 0.64 bl s)1, aftersubtracting currents from ground speeds (Rose et al.,1995).

The movements shown in Fig. 4 are characteristicfor cod in the Bornholm Basin during the codspawning season (Aro, 1989; Wieland et al., 2000). In2003, the availability of oxygen in deep layers (ICES2004) allowed the cod to migrate into the deep centralpart of the basin. However, in other years with dif-ferent hydrographic situations, lack of oxygen in thebasin centre will likely result in cod spawning else-where, including the basin slopes or the western BalticSea. Our geolocation method is suited to alert man-agers whether closed areas need to be adjusted giventemporal and spatial differences in cod spawning dis-tribution under different environmental scenarios.

Using our method, it will be difficult to track lim-ited westwards migrations, for example from the eastcoast of Bornholm to the west coast. The east-westsalinity and temperature gradients at depth are notvery distinct in the absence of high-salinity North Seawater inflows. However, long distance migrations, asthe one displayed in Fig. 7, are detectable. This indi-vidual displays an unusual behaviour. Such long dis-tance migrations from the eastern to the westernBaltic have been observed before (Bagge et al., 1974;Berner, 1981), but are considered to only rarely occur(Otterlind, 1985). Although the Arkona basin west ofBornholm is a known mixture area for the eastern andwestern Baltic cod stocks, eastern Baltic cod are notthought to venture further to the west. West of theArkona basin, the cod usually differ morphometrically(Berner and Vaske, 1985) and genetically (Moth-Poulsen, 1982; Nielsen et al., 2003) from easternBaltic cod.

Eastward migrations to other basins can only beidentified if the cod stayed below the halocline in thecentral basin during migration. In the Bornholm Basinthe area below the halocline is between 45% and 65%of the total basin area, dependent on the hydrographicsituation (corresponding to the area within the 40 and60 m depth contours). The data in Fig. 3 indicate thatthis particular cod stayed at depth greater than 60 mbetween day 160 and recapture at day 242. There is nogeneral period characterized for which cod definitely

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stay below the halocline during the year. However,cod eggs need at least salinity around 11 to be fertilizedsuccessfully and to survive (Nissling and Westin, 1991;Westin and Nissling, 1991). Hence, there is a strongindication that adult cod will migrate to below thehalocline during the spawning season, between Marchand October (Wieland et al., 2000). Consequently,eastwards migrations, for example out of the basinafter spawning, will be detectable by our method.

The method we presented here is in principalapplicable to other regions with horizontal gradients,such as semi-enclosed seas (e.g. Gulf of St Lawrence,Black Sea, fjords and in areas with distinct fronts orriver plumes). Geolocation accuracy is limited by thestrength of the horizontal gradients. There are nostructural limitations, as are the case for light-basedgeolocation around the spring and fall equinoxes (whenday length is almost 12 h at all latitudes). For example,for light-based geolocation attempted at 50�N andwithin 5 days of an equinox, a misspecification in daylength of 1 min resulted in an error of over 150 km inestimated latitude (Welch and Eveson, 1999).

The combined use of electronic tags and hydrody-namic modelling can potentially improve the man-agement of the Baltic cod stocks in terms of closedareas and stock separation. The method will also allownew insights into the migratory behaviour of Balticcod, including swim speeds and starting-and-stoppingbehaviours. These insights may facilitate the investi-gation of environmentally driven changes in migratorybehaviour and their consequences for populationprocesses including cod recruitment and cod predationon other fish.

ACKNOWLEDGEMENTS

This study was part-funded by DG Fisheries of the EUunder the CODYSSEY project (contract code Q5RS-2002-00813). We would like to thank C. Broberg andO. Larsen for their valuable help at sea, Skipper C.Holm for sharing his knowledge on the fishing groundsaround Bornholm, and N. Andersen and J.E. Beyer foradvice on cod handling and useful discussions. Fur-thermore we would like to thank B. MacKenzie andthree anonymous reviewers for their valuable com-ments on earlier version of this manuscript.

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