Original Article

Identifying the location and importance of spawning sites ofWestern Baltic herring using a particle backtracking model

R. K. Bauer1,2*, U. Grawe3, D. Stepputtis1, C. Zimmermann1, and C. Hammer1

1Thunen Institute of Baltic Sea Fisheries, 18069 Rostock, Germany2Ifremer, UMR 212 EME, BP 171 34203 Sete Cedex, France3Leibniz Institute for Baltic Sea Research Warnemunde, 18119 Rostock, Germany

*Corresponding author: e-mail: robert.bauer@ifremer.fr; daniel.stepputtis@ti.bund.de

Bauer, R. K., Grawe, U., Stepputtis, D., Zimmermann, C., and Hammer, C. 2014. Identifying the location and importance of spawning sites of WesternBaltic herring using a particle backtracking model. – ICES Journal of Marine Science, 71: 499–509.

Received 29 April 2013; accepted 6 September 2013; advance access publication 13 December 2013.

The recruitment success of some herring stocks fluctuates strongly, and apparently, success is often already determined during the early lifestages, i.e. before metamorphosis. In studying the survival of early life stages and its affecting factors, particularly those during the egg stage,it is crucial to examine the processes at the spawning sites, which often cannot be explored directly. A recent decline in the recruitment ofWestern Baltic spring-spawning herring (WBSSH) increases the urgency of filling the knowledge gap for this stock, especially because onebottleneck in the recruitment seems to occur before hatching. We examined the successful 2003 –2009 spawning sites of WBSSH in themain spawning ground, the Greifswalder Bodden lagoon. Instead of using common techniques such as diving or underwater videography,which are usually unsuitable for mapping large areas, we applied a model approach. We tracked herring larvae at length 6–10 mm, recordedby larval surveys during March–June of the respective years, back to their hatching sites using a Lagrangian particle backtracking model. Wecompared the spawning areas identified by the model with the results of earlier field studies; however, we also analysed variations betweenyears, larval length groups, and different applied growth models, which are needed to define hatch-dates. Although spawning sites couldnot be identified with high precision because of the strong diffusion in the area studied, results indicate that larvae up to 10 mm length arecaught near their hatching sites. However, the location of successful spawning sites varied largely between years, with the main hatchingsites situated in the Strelasund and the eastern entrance of the lagoon. This may reflect variations in spawning-site selection or quality. Abetter knowledge of the locations and relative importance of, and the processes occurring on, the different spawning sites will provide animportant contribution to the sustainable management of this commercially valuable herring stock.

Keywords: backtracking, Baltic Sea, Clupea harengus, hatching sites, lagoon, larval transport, spring spawning.

IntroductionThe identification of spawning sites of exploited fish stocks is par-ticularly important for recruitment and conservation in fisheriesmanagement. This knowledge can be used to protect sensitive habi-tats (Hammer et al., 2009), establish marine protected areas, orassess recruitment by conducting larvae and egg surveys in relevantareas. Spawning grounds, especially for migratory fish species, areusually assumed to have relatively fixed locations. Spawning-site fi-delity has been discussed for Atlantic and Pacific herring and is con-sidered an important component in the structuring of herringpopulations (Sinclair and Tremblay, 1984; Wheeler and Winters,1984; Blaxter, 1985; Flostrand et al., 2009). However, some

long-term studies contradict these hypotheses, revealing a signifi-cant variation in the spawning grounds utilized [e.g. Dickey-Collas et al. (2001) for the Irish Sea; Munk and Christensen(1990) and Schmidt et al. (2009) for the North Sea; and Hay et al.(2009) off British Columbia]. Identifying spawning sites of migra-tory species, such as herring, and their relative importanceremains a challenge. Various methods are used, although each hasits limitations. For demersal spawning herring, common techniquesinclude diving and underwater videography (Scabell, 1988; Aneer,1989; Kaaria et al., 1997; Hammer et al., 2009), which are unsuitablefor mapping large areas. Other methods include remote sensing ofspawning substrata, such as macrophytes, and the incorporation

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Marine ScienceICES Journal of Marine Science (2014), 71(3), 499–509. doi:10.1093/icesjms/fst163

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of other spawning-site characteristics (Hammer et al., 2009;Naslund et al., 2011). However, because macrophytes mightpersist only during the spawning season or for a limited numberof years, spawning-site selection can have significant intra- andinterannual variations, limiting the representativeness of suchstudies. Recently, particle backtracking has gained attention for itsability to identify spawning sites of fish stocks (Christensen et al.,2007, 2008). In such an application, larvae are tracked back in simu-lated flowfields as parameterized drifters, from areas of known oc-currence or catch. Although this method allows rapidexamination of the location of successful multiyear spawningsites, the precision of estimating locations may be limited.Contrary to expectation, dispersed larval distributions cannot beconverged to a common origin, because dispersive and stochasticprocesses must be considered in the context of the model simula-tions. In this regard, Christensen et al. (2007) distinguishedbetween an ideal “inverse time simulation” and a “reverse timesimulation”, where dispersion is taken into account and hatchingsites were thus identified as more or less accurate spatial probabilitydistributions. Despite this limitation, reverse time simulationshelped locate spawning sites of sandeels in the North Sea and areprobably also useful in cases where larvae are released into asteady flow regime. Their applicability, however, remains to betested in diffusion-dominated circulation systems.

The Greifswalder Bodden (GWB), a shallow lagoon of thewestern Baltic Sea south of Rugen Island, is an example of such anenvironment (Figure 1; total surface area of GWB: 514 km2;Schiewer, 2008), i.e. drift and circulation patterns can changerapidly as a result of frequent changes in prevailing wind conditions,particularly in its direction (Bauer et al., 2013). This lagoon isfurther considered to represent the main spawning ground ofWestern Baltic spring-spawning herring (WBSSH) (Jonsson andBiester, 1981; Biester, 1989) and therefore has been monitored con-tinuously during the entire spawning period (March–June) since1977 in the framework of the Rugen Herring Larvae Survey(RHLS; Oeberst et al., 2009a). This assumption is supported bythe fact that estimated annual abundances of 20 mm larvae in theGWB, obtained from RHLS, correlate well with later recruitmentestimates, such as the abundance of age 1 and 2 herring, derivedfrom acoustic surveys, and VPA-derived estimates of age 0 recruit-ment (Oeberst et al., 2009a). The time-series of the index calculatedfrom the survey, however, reveals strong fluctuations in the recruit-ment of WBSSH. In the period 2004–2008, a continuous declinewas recorded, with an average annual decline of 15–35%, thecauses of which remain unknown (ICES, 2011). In contrast to theabundance and distribution of larvae, little attention has beenpaid to the processes taking place on spawning sites, specificallythe mortality of eggs and related effects on WBSSH recruitment.As one bottleneck in the recruitment appears to occur before hatch-ing (Polte et al., 2013), such an investigation appears relevant butrequires a detailed identification of the location of spawning sites.Potential macrophyte spawning grounds are present along almostall edges of the pan-shaped GWB lagoon but are not used uniformlyfor depositing eggs (Figure 1; Hammer et al., 2009). It is currentlyunclear why certain macrophyte assemblages are used as spawninggrounds and others are not. The specific location of the macrophytebeds may be decisive for egg survival because of, inter alia, currentand wave-surge exposure (e.g. oxygen supply or mechanical de-struction). The latest studies of WBSSH spawning sites were con-ducted by Scabell (1988) and Hammer et al., (2009); however,they provided no information on potential intra- and interannual

variations in spawning habitat abundance and utilization. Such var-iations are likely to exist for WBSSH because the spatial distributionof early larval stages in GWB varies significantly within and betweenyears. By applying a particle backtracking model, we therefore focuson examining (i) the location of successful spawning sites ofWBSSH during 2003–2009, and (ii) spawning-site fidelity duringthis period. We compare the results with previous field investiga-tions and discuss the suitability of particle backtracking modellingto perform these tasks.

Material and methodsWe examined hatching sites of all WBSSH larvae of size classes 6–10 mm sampled weekly at up to 35 stations during RHLS betweenMarch and June in the period 2003–2009. For this purpose, weapplied an offline Lagrangian particle tracking model to track simu-lated larvae back to their hatching sites.

Particle backtracking modelTo simulate larval dispersal, it is crucial to consider larval behaviour,because vertical migrations can affect the speed and directionof larval drift. However, information on specific diel vertical mi-gration patterns of WBSSH larvae in the GWB is lacking, andcannot be transferred easily from other study areas because theydiffer considerably between herring stocks and regions, and arefurther likely to be size-specific (Schnack, 1972; Johannessen,1986; Munk et al., 1989). Recent field investigations in the GWBindicate that early stages of WBSSH larvae exhibit a near-surface dis-tribution (P. Polte, pers. comm.) that is in agreement with observa-tions from other shallow-spawning herring stocks (Stevenson, 1962;Johannessen, 1986). We therefore assumed that larvae are releasedimmediately to the water column after hatching and remain in theupper 6 m. This depth approximately equals the average depth ofthe GWB and also the lower depth limit of its macrophyte coverage(Scabell, 1988; Hammer et al., 2009). We disregarded possible ver-tical migration patterns. Instead, we assumed larvae to encountera depth-averaged flow caused by the pronounced vertical mixingin the lagoon. The offline particle backtracking model used(Grawe and Wolff, 2010) was therefore forced by calculatedhourly depth-averaged flowfields of the upper 0–6 m watercolumn of the GWB area. Appropriate flowfields for model years2003–2009 were obtained from a three-dimensional, triple-nestedcirculation model (Figure 1), where the innermost nested modeldomain covers the GWB area with a horizontal resolution of180 m and 16 vertical layers. A more detailed description of themodel set-up and validation is given in Bauer et al. (2013).

For everysampling record (287–353 per year), we released larvae—parameterized as Lagrangian drifters—in simulated flowfields at thetime of sampling and at the grid point closest to the sampled surveystation. We set the number of seeded particles constant to 100 000,regardless of the number of larvae caught during the specific samplingevent, to achieve a sufficiently resolved picture of the larval dispersalfrom each release, and so the possible origin of larvae. Based ontrial runs, this number seemed appropriate to the 35 039 total gridpoints of the model domain. Particle tracking results were stored asconcentration fields with a temporal resolution of 1 h.

Growth modelWe defined the tracking duration of larvae as the time (in hours)needed to reach the hatching sites. However, the specific age, andhence the hatch-dates, of sampled larvae was unknown. The track-ing duration was therefore defined as a function of larval growth.

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Modelling larval growth is one of the most difficult tasks of advancedindividual-based models (IBMs; Hauss and Peck, 2009). As larvalgrowth rates exhibit high spatio-temporal variability and are size-and stock-specific, a detailed knowledge of growth-affectingfactors (e.g. food density and size), their spatio-temporal variation,and larval physiology is required. However, for WBSSH larvae, thisinformation is rare. Therefore, instead of applying a more detailedIBM, we used a comparative approach of different growth models.First, we used a temperature-dependent, mean-daily growthmodel for WBSSH larvae (Oeberst et al., 2009b) given as

G = 0.011 + 0.037 T (r2 = 0.51). (1)

Here, G is the growth rate in mm d21 and T the local temperatureexperienced by larvae. Second, we applied a constant-growthmodel with two different growth rates (0.2 and 0.3 mm d21).

Based on additional model runs in which particle temperatureexposure was recorded, we estimated temperature-dependentlarval growth rates of different releases. Here, only one example par-ticle was released, disregarding differences in the temperature ex-posure among particles, so reducing the computational effort.Growth rates, estimated in this way, can vary considerably duringthe spawning season, a known feature of herring larval ecology,reaching rates of 0.38–0.57 mm d21 at 10–158C. Although theserates are still in the range of reported rates (Oeberst et al., 2009b),they might be too high for early larval stages. Busch et al. (1996)described growth rates of WBSSH yolk-sac larvae as rangingbetween 0.11 and 0.17 mm d21 in the early spawning season(March) and between 0.25 and 0.38 mm d21 in the later spawningseason (May), which is in accordance with findings of otherstudies of spring-spawning herring larvae (Checkley, 1984;Oeberst et al., 2009b). For example, Henderson et al. (1984)

Figure 1. GWB area with circulation model domains (upper right corner; BSM, Baltic Sea model; WBSM, western Baltic Sea model; GWBM,Greifswalder Bodden model), a detailed view of the GWBM, RHLS stations (blue dots), and strata (I –V, KB, Kubitzer Bodden; B, Baltic Sea), reportedWBSSH spawning sites by Scabell (1988; green and red) and macrophyte coverage (light green), mapped by aerial photography in 2009, adaptedfrom Hammer et al. (2009). Map adapted from Bauer et al. (2013).

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described growth rates of 0.18 mm d21 for yolk-sac larvae and0.43 mm d21 for post-yolk-sac larvae of Thames Estuary andBlackwater herring. In contrast, both constant-growth rates are rela-tively conservative estimates for fast-growing, spring-spawningherring larvae and are therefore used as comparative values.

Length- and time-dependent hatching probabilitiesIn addition to growth rates, hatch-lengths are necessary to estimatelarvae hatch-dates. The latest study of the hatch-lengths of WBSSHlarvae was conducted by Klinkhardt (1986) (Figure 2), which indi-cated that hatch-lengths can differ by up to 2 mm and follow anormal distribution (Kolmogorov–Smirnov test, p ¼ 0.2875,D ¼ 0.071).

To account for this variability, we did not consider thehatch-lengths, and hence the hatch-dates of larvae, as a fixedlength or time but as normally distributed. We weighted the positionof larvae at a specific time-step according to the probability that thelarvae length at this time-step represented the actual hatch-length.To obtain the time-specific hatching probability, we fitted anormal distribution to the hatch-length distribution postulated byKlinkhardt (1986) and subsequently described it as a function oftime (Figure 2). The median hatch-date was defined as the time atwhich larvae reached the median hatch-length t(L), whereas thehatch-date variance was further estimated by the length of minussigma t (L2s) and plus sigma t (L+s); s represents the standard de-viation of the hatch-date distribution, giving the variance in time-steps as

s2 = (t(L−s) − t(L))2 + (t(L+s) − t(L))2

2. (2)

This procedure was applied to every release and implies that thewidth of hatch-date distributions of the temperature-dependentgrowth model could differ significantly between releases, becausethe defined distribution estimates t(L), t (L2s), and t (L+s) werereached at different time-steps, depending on ambient water

temperatures. Therefore, slow-growing cohorts (releases) demon-strated a broader hatch-date distribution than fast-growingcohorts. In contrast, the distribution parameters (median and vari-ance), and therefore distributions of all evaluated cohorts, were con-stant for both constant-growth models. From these probabilitydistributions, we calculated hatching probabilities Pt for all time-steps t between the minimum and maximum (5.5 and 7.3 mm)described by Klinkhardt (1986), using the probability density func-tion. We standardized these estimates by the sum of all calculatedprobabilities, so that the sum of all probabilities of incorporatedtime-steps was adjusted to 1.

Subsequently, we used time-dependent hatching probabilities asaweighting factor for the concentration fields. The hatching site H oflarvae of a specific length group L, caught at the specific station S andtime T, was defined as

HL,S,T =∑

t

CL,tPt, (3)

where CL,t gives the concentration field of the particle distribution attime-step t, and Pt denotes the probability of the time-step to repre-sent the hatch-date.

Particle spreadingDepending on the degree of dispersion of a larval patch, the numberof particles can vary significantly between grid points. Typically, thisdegree increases with time. As a consequence, aggregated distribu-tions, and therefore early positions, are more accurate and so aregiven greater weight than dispersed patches, which occurred atlater time-steps. However, this poses a problem because both earlyand later particle positions may be equally likely or unlikely, accord-ing to the hatch-length distribution (Figure 2). To minimize thiseffect and to facilitate a comparison of aggregated and dispersed dis-tributions, we log-transformed particle concentration fields, afterincreasing them by 1 to avoid negative results. As a result, the infor-mation of particle spreading was preserved, but the range of particleconcentrations per grid point could not exceed values .11.52[�log(100 001)].

Finally, to obtain the total weighted contribution of all hatchingsites per year HY, all hatching areas of a specific year estimated thisway were summarized after being multiplied by the relatednumber of larvae caught at the specific release station and timeNL,S,T, thus accounting for the success of different spawning sites:

HY = ln(HL,S,T + 1)NL,S,T .

From the results obtained, we examined intra- and interannual var-iations of estimated hatching sites, along with related larval driftrates and distances, and compared them with results of previousstudies of WBSSH hatching sites conducted by Scabell (1988) andHammer et al. (2009). The visualization of estimated hatchingsites of larvae, however, requires a selection of modelled results.Therefore, the results presented in the following sections focusmainly on model year 2006 and 8 mm larvae. We chose the lengthof 8 mm because it is close to hatching but does not overlap thehatch-length distribution, and corresponding larval abundancesappear less patchy than those of smaller length groups. For thislength group, the relative importance of different spawning strata(Figure 1) was calculated, and potential links between the locationof successful hatching sites and the N20 recruitment index wereexamined.

Figure 2. Hatch-length distribution of WBSSH larvae in April and May1983, redrawn from Klinkhardt (1986). The solid line indicates fittednormal distribution.

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Sensitivity of the flowfieldApart from the application of different growth models affectingtracking durations, the sensitivity of results obtained was tested rela-tive to the flowfields utilized. In the first sensitivity experiment, weassumed that particles only experience the flowfield of the upper3 m, not of the upper 6 m. This indicates the importance of thedirect effect of wind. In a second set of sensitivity experiments, weinvestigated mesoscale effects of the flowfield on the particle spread-ing. This could be done because the applied circulation model usedto generate the flowfields is able to resolve mesoscale eddies by virtueof its high resolution of 180 m. However, the generation of theseeddies can be seen as a random process, and a single model run isonly one realization of such a stochastic process. To account forsuch randomness, the initial conditions of the ocean model wereperturbed, while still applying the same atmospheric and boundaryforcing, resulting in different flowfields. By calculating three of theseperturbed flowfields, we could quantify the effect of eddies on thetransport and spreading of the particles. Sensitivity runs were con-ducted only for 2006.

Computational effortIn summary, we investigated 62–311 sampling events per modelyear, length group, and growth model, resulting in 21 093 evaluatedrelease events of Lagrangian tracers and an additional 7031 separatereleases to assess temperature-dependent growth rates. To reducethe related computational effort, model runs were conducted onlyonce for all length groups caught at a specific sampling stationand time. This required the simplified assumption that larval drift-ing characteristics do not differ for small larvae (≤10 mm).Therefore, we defined the tracking duration of every model run tolast at least until larvae of 10.5 mm (the largest length group evalu-ated) had reached the lower hatch-length of 5.5 mm, regardless ofthe applied growth model. In this way, the number of Lagrangianmodel runs could be reduced significantly to only 2146 each.

Drift rates and drifted distancesTo further improve our understanding of the wind-dominatedlarval dispersal in such semi-enclosed lagoons as the GWB, weexamined drift rates of larvae and their linear distance to hatchingsites. To obtain drift rates, we calculated drifted distances of particlesand defined them as the length of drifted trajectories from theirrelease stations (catch positions of larvae). For this purpose, theLagrangian particle backtracking model was extended by a “drift-distance module”, recording the covered distance of particles pertime-step. Thus, we derived hourly, weekly, and monthly larvaldrift rates, specified as the distance covered within the given interval,from each Lagrangian model run. In addition, we calculated lineardistances of particles to their release positions (catching sites oflarvae). To account for the non-uniform, cloud-like spreading ofparticles, we applied results obtained from model runs conductedunder the Eulerian framework. We calculated linear distances ofevery grid point of the model domain to the particle release stations.The resulting distance matrix was weighted by the time-dependentspatial distribution of particles, giving the time-dependent distancesof particles to their release sites.

ResultsLarval abundances obtained from the narrow grid of survey stationsreveal a strong intraannual variation in the amount and location oflarvae caught (Figure 3, left panel). The spawning season of WBSSH

is relatively short, with the peak of larval production commonly oc-curring within a few weeks. Especially small larvae (≤10 mm),which are the focus of this study, appear more patchy than evenlydistributed. In 2006, two spawning peaks occurred, one during cal-endar weeks (CWs) 17–18 and one in CW 21 (Figure 3). High abun-dances of larvae were detected in the eastern and western parts of thelagoon, including the adjacent Strelasund.

Results from backtracking simulations of larvae of different sizeclasses found during several years in GWB and the Strelasund dem-onstrate that most of these larvae originate in this area and not in thesurrounding Baltic Sea (Figures 4 and 5). Drift rates of larvae nor-mally range between 0.13 and 0.45, 0.18 km h21 on average, andare generally below 1.5 km h21. Therefore, weekly drifted distances,the length of trajectories, account for an average of 28.1 km and amaximum of 65 km (Figure 6a). Despite the comparatively greatdistance drifted, larvae remain closer to their release stations, onaverage within a linear distance of 5.1 km after 1 week and 11 kmafter 1 month of backtracking (Figure 6b). The linear distance torelease positions can be expressed as a saturation function, with11 km representing the asymptote of average distances to catch posi-tions. Therefore, larvae originate close to catch positions. However,owing to the proximity of many potential spawning sites, the specifichatching sites cannot easily be determined from the sampled larvaldistribution. On a larger scale however, particularly the Strelasundand the eastern part of the lagoon (with its transition to the BalticSea) appear to feature pronounced hatching sites. Simulations con-ducted for several spawning seasons (2003–2009) confirm theseresults, though they reveal that both areas are of varying importance(Figures 5 and 7). Further, the time-series reveal no trend in the lo-cation of successful spawning sites but a pronounced variability,which, however, seems unrelated to the observed recruitmentdecline of WBSSH (Figure 7).

Estimated hatching sites obtained from different growth modelsand length groups are in broad agreement, although tracking dura-tions could differ significantly. Owing to the warming of the water,daily temperature-dependent growth rates rapidly exceed the twoconstant-growth rates within each spawning season. As a result ofoften shorter drift duration, estimated hatching sites are more con-fined when derived from temperature-dependent growth modelsthan when obtained from constant-growth models. However, allapplied growth models indicate that larvae could also originate inareas where spawning appears unlikely, particularly in the centreof the lagoon where the main spawning substratum, macrophytes,is missing (see Figure 1 for comparison). Effects of the applied flow-fields (0–3 and 0–6 m) on the location of hatching sites are not sub-stantial (Figure 3) and even imperceptible for perturbations in themesoscale of the outer Baltic Sea (not shown).

DiscussionThe larvae of WBSSH caught in the GWB area during weekly surveyswere found to originate close to their catch positions. In this context,drifted distances (length of the drift pathway) appear remarkablygreat. This discrepancy can be attributed to the great wind-inducedvariability of flowfields in the area studied, described by Bauer et al.(2013), causing larvae to drift in an oscillating manner and thus beretained. The location of estimated hatching sites obtained from dif-ferent growth models and flowfields (0–3; 0–6 m with or withoutperturbations in the mesoscale of the outer Baltic Sea) is in relativelygood agreement, further highlighting the degree of larval retentionand emphasizing that the position of larvae in the water column aswell as the exact drift duration are less important. In contrast, the

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Figure 3. Calculated hatching sites in 2006 for 8 mm larvae, and related larval abundances (from survey data) recorded during RHLS in different CWs. Left panel: total abundance (number m22) ofWBSSH larvae at a given CW in 2006 for each survey station. Other panels: calculated hatching sites described for two different flowfields (0–3 and 0–6 m depth-integrated) and three different growthmodels (temperature-dependent growth, constant growth at 0.2 and 0.3 mm d21). The median temperature-dependent growth rates are given for each CW.

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spatial heterogeneity of located hatching sites of different lengthgroups appears more pronounced, particularly between lengthgroups 6–8 and 9–10 mm. These differences may reveal uncertain-ties of the model but are more likely related to differences in theinitial larval distribution, as local abundance peaks clearly diminishand larvae are distributed more homogeneously with increasingsize. This in turn could be a consequence of spatially inhomogen-eous and density-dependent larval mortality rates. It is likely thatthe differences in the larval distribution obtained from larvalsurveys result in a different weighting of identified hatching sites.As a result of the latter, hatching sites of different size classes maynot necessarily overlap. However, it is clear that the estimate ofhatching sites is also affected by larval dispersion as stated byChristensen et al. (2007), causing significant uncertainties, particu-larly for older and hence longer-drifted larvae. Therefore, back-calculated hatching sites appear more as diffusive clouds thanprecise locations. Accounting for the uncertainties mentionedabove, the results indicate that, in some cases, spawning may alsooccur in the deeper central part of the lagoon. This appears unlikelyas the area is not currently covered by macrophytes, the main spawn-ing substratum, except a small group of boulders or dropstones, the“großer und kleiner Stubber” (Hammer et al., 2009); rather, thearea is characterized by soft sediments (Katzung, 2004). However,owing to this exception and the sporadic availability of largerboulders (Figure 1), which can also serve as spawning substratum

(Klinkhardt, 1996; Hammer et al., 2009), successful hatching inthe central lagoon cannot be excluded entirely. In fact, fishersreport spawning in deeper parts of the lagoon, because gillnets setin the area are often covered by spawn, a behaviour previouslydescribed by Scabell and Jonsson (1984). Still, the low probabilityof the occurrence of hatching sites in shallow water and smallcoves, estimated from particle backtracking experiments, is striking,because these areas are well covered by macrophytes and were pre-viously identified as important spawning sites. This may reveal amethodological problem with the backtracking approach, becausedispersion effects often act as a one-way street for drifting larvae,making it easy to leave but not to enter isolated or high-energy envir-onments. The latter refers especially to shallow waters, which aremuch more reactive to changes in windforcing than the calmerand deeper central part, intensifying larval dispersion. Results ofback- and forward-tracking experiments therefore must not leadto the same results (Christensen et al., 2007). Despite these con-straints, the results give a rough idea of the location of spawninggrounds in the GWB area. Generally, they highlight the importanceof the western (Strelasund) and eastern entrances to the lagoon.Both of these areas are migration channels of adult prespawningherring to the inner GWB, of which the Strelasund is particularlyof varying importance (Jonsson and Biester, 1981; Jonsson andRichter, 1993). Larval abundances in the Strelasund also demon-strate significant variability. Based on extensive tagging experiments

Figure 4. Calculated hatching sites in 2006 and annual larval abundances (from survey data) of five different size classes (6–10 mm larvae). Leftpanel: total abundance (number m22) of WBSSH larvae in 2006 for each survey station and length group. Other panels: calculated hatching sitesdescribed for three different growth models (temperature-dependent growth, constant growth at 0.2 and 0.3 mm d21).

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in the 1980s, and in agreement with the model results presentedhere, Jonsson and Richter (1993) described that spawning takesplace almost immediately after herring enter the lagoon byits eastern entrance. It is interesting that, there and withinthe western entrance (the Strelasund), currents are generallypronounced. WBSSH spawning-site selection may therefore be trig-gered not only by the availability of spawning substrata (macro-phytes) but also by a certain exposure to currents, which are likelyto provide sufficient oxygen and avoid egg sedimentation. Such apreference for spawning-site characteristics has been reported forother herring stocks (Haegele and Schweigert, 1985). Although mi-gration patterns of WBSSH have been described as relatively con-stant over the years, changes in the locations of successfulhatching sites are evident. This could be the result of spatially in-homogeneous mortality rates but could also indicate respective

changes in spawning-bed utilization and thus contradict consider-able spawning-site fidelity for WBSSH. Although this certainlyremains a question of geographical scale (Hay et al., 2001), varia-tions in hatching-site location could also reflect changes inspawning-site quality. Addressing this question appears to be of par-ticular interest because it could help identify factors responsible forWBSSH recruitment fluctuation. It is therefore recommended toquantify spawning-site selection, e.g. as the amount of spawn perspawning site per year. Studies could be conducted on bothlagoon inlets and focus on the emergence of immigrating spawnersand later to larval abundances. The uncertainties mentioned abovedemonstrate the limitations of backtracking models, precisely“reverse time simulations”, as a tool for identifying spawning sites.It is not possible to determine well-defined hatching sites indiffusion-dominated circulation systems, even when using early

Figure 5. Calculated hatching sites of 8 mm herring larvae and related annual larval abundances (from survey data) during 2003–2009. Left panel:total abundance (number m22) for each survey station. Other panels: calculated hatching sites described for three different growth models(temperature-dependent growth, constant growth at 0.2 and 0.3 mm d21).

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larval stages. The complex geographical and hence hydrologicalstructure of the lagoon studied further impedes this task. Anotherapproach would be to conduct forward particle-tracking experi-ments from diverse spawning grounds and subsequent comparisonswith the larval abundances measured. This may help solve some ofthe problems (Christensen et al., 2007), e.g. the one-way streets ofshallow waters and coves found around the lagoon. The resultsfurther illustrate the importance of larval mortality in comparinghatching sites for several length groups. More advanced approachesshould address these effects, particularly density-dependent andspatio-temporal changes in larval mortalities, as well as larval behav-iour, because it can affect the precision of estimated larval dispersionpatterns. Here, changes in the drifting characteristics during the

larval development should be considered, especially buoyancy andvertical migration patterns (Schnack, 1972; Johannessen, 1986). Inthis context, the yolk-sac stage appears especially important,because it can last for a considerable time (4.4–11.6 d for WBSSHlarvae; Busch et al., 1996). However, larval behaviour during thisstage remains poorly understood, and studies indicate significantdifferences to later larval stages, with the vertical distribution ofyolk-sac larvae being less variable (Johannessen, 1986), and morerelated to the seabed, owing to their relatively poor swimming per-formance and greater specific weight (Pilz, 1986). These factorscould increase larval retention at the spawning sites and thereforesignificantly reduce the real “drifting period”, and thus larval disper-sion. A better understanding of these processes will improve thebacktracking of larvae considerably, especially of early stages(,8 mm), which overlap with the hatch-length distribution. Inthis context, changes in the hatch-length distribution, particularlywithin years, caused by differences in encountered incubation tem-peratures (Blaxter and Hempel, 1963; Busch et al., 1996), should alsobe considered because they can result in variations of more than2 mm. Related inaccuracies are therefore likely to have moreserious effects on the backtracking results than the precision of thegrowth model. Related to the suggestions mentioned above, thedetailed data on larval abundances in the GWB provide a good op-portunity for the further development, and thus improvement, oflarval-particle (back-)tracking models. In the light of this, the ap-proach presented here can be seen as another step towards a better

Figure 6. Box-and-whisker plots, showing the time-dependent drifteddistance (a) and linear distance to catch positions (b) of larvae. The lineindicates the linear relationship between absolute drifted distance andduration.

Figure 7. Relative contribution of the seven spawning area stratadefined in Figure 1 to the overall annual spawning success in the GWBarea, measured as abundance of 8 mm herring larvae, 2003–2009 (sumof each column ¼ 100%). Strata: KB, Kubitzer Bodden and adjacentBaltic Sea; I–V, RHLS survey strata (Oeberst et al., 2009a), comprisingthe Strelasund (I), Northwestern (II), Southwestern (III), Southeastern(IV), and Northeastern (V) GWB; B, Baltic Sea. Lower panel: N20recruitment index as derived from RHLS and used in the ICESassessment (Oeberst et al., 2009a; ICES 2011).

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understanding of herring larval ecology in shallow lagoons in theBaltic, after the significant insights gained by Bauer et al. (2013)on larval retention.

The results of this study also demonstrate that the importance ofthe location of successful hatching sites varies largely. Although theprecision of determining the relative importance of spawning sitesretrospectively within GWB seems to be sufficient, the results ofthis study cannot be used to predict their importance in future.Further, the results do not reveal a link between the geographicalposition of important spawning sites and spawning success in a spe-cific year. It can be concluded therefore that protective measuresshould target not only a particular spawning site but rather thelagoon’s entire shallow-water area, and so the vast majority ofWBSSH spawning sites.

AcknowledgementsThe research was partly financed by and conducted as part of theFehmarn Belt Science Provision Project. Additional financialsupport by the Anker Stiftung, Dassow, is gratefully acknowledged.The work of UG was funded by the BMBF of Germany through grantnumber 01LR0807B.

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