Biophysical modeling of survival and dispersal of Central and Eastern Baltic Sea flounder (Platichthys flesus) larvae

Journal of Sea Research 142 (2018) 11–20

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Biophysical modeling of survival and dispersal of Central and Eastern Baltic Sea flounder (Platichthys flesus) larvae

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Hinrichsen H.-H.a, , Petereit C.a, von Dewitz B.a, Haslob H.b, Ustups D.c, Florin A.-B.d, Nissling A.e a

GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, Kiel 24105, Germany Thünen Institute of Sea Fisheries, Palmaille 9, 22767 Hamburg, Germany c Institute of Food Safety, Animal Health and Environment BIOR, Daugavgrivas str 8, Riga LV-1048, Latvia d Institute of Coastal Research, Department of Aquatic Resources, Swedish University of Agricultural Sciences, Skolgatan 6, 742 42 Öregrund, Sweden e Ar Research Station, Department of Ecology and Genetics, Uppsala University, Visby SE-621 67, Sweden b

A R T I C LE I N FO

A B S T R A C T

Keywords: Hydrodynamic modeling Particle tracking Flounder juvenile habitat suitability Sediment type-related mortality Larval drift

The period of larval drift into a suitable nursery area is considered to be of great significance for recruitment variability in flatfish. Here, a hydrodynamic model coupled with a Lagrangian particle tracking technique was utilized to study the drift from the first feeding larval stage until time of settlement of Central and Eastern Baltic flounder (Platichthys flesus), originating from spawning in the Baltic Sea deep basins, the Arkona- and Bornholm basin (central Baltic Sea), and the Gdansk deep and Gotland basin (eastern Baltic Sea). We examined the spatiotemporal dynamics of the probability to settle in preferred nursery habitat by detailed drift model simulations. The study suggests that the majority of larvae (89% and 74% for Central- and Eastern Baltic flounder, respectively) drift towards coastal areas and settle at metamorphosis ≤20 km from a sandy habitat enabling further migration to a preferred nursery area, i.e. larval drift seems not to be a major bottleneck in recruitment of flounder spawning in the Baltic Sea deep basins. The drift model results suggest that Central Baltic flounder utilize nursery areas mainly in the central and western Baltic, and in the Kattegat, whereas Eastern Baltic flounder mainly utilize the coast in the central and eastern Baltic. Thus, the two stock components seem to use different nursery areas following settlement. Further, in accordance with the “nursery size hypothesis”, the model demonstrates that larvae from the Bornholm basin, utilizing areas with extensive distribution of preferred nursery habitat, display the highest relative successful transport to nursery grounds until settling (72% of successfully settled larvae), suggesting that spawning in the Bornholm Basin is of great importance for stock recruitment of deep basin spawning Baltic flounder.

1. Introduction Development of fish stock size is the result of variability in recruitment and in mortality of adults. Hence, knowledge about the connectivity between the adult stock and the recruits, i.e. between spawning- and nursery areas is a prerequisite for understanding stock development. Poorer stock development nowadays may be caused (among other processes) by habitat degradation, influencing both the quantity and quality of spawning- and nursery areas (Levin and Stunz, 2005; Sundblad et al., 2014), i.e. habitat degradation may contribute to the ongoing decline in fish stock abundance (Worm et al., 2006; Seitz et al., 2014). Hence, the functioning of spawning and nursery areas could be a crucial bottleneck for stock development and identification of crucial habitat for recruitment (Beck et al., 2001; Seitz et al., 2014), including spawning- and nursery areas is essential for conservation

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issues, marine spatial planning, and for sustainable fisheries management. It is the basis for an ecosystem based approach in the management of fish resources (Rosenberg et al., 2000), e.g. the implementation of MPAs (Sundblad et al., 2011) and restoration of formerly important nursery grounds (Le Pape et al., 2014) to sustain fish stock development. Flatfishes display a complex life cycle including an ontogenetic metamorphosis when larvae develop from being bi-laterally symmetric to asymmetry and shift from a pelagic to a demersal habitat. This shift normally occurs when larvae reach coastal areas and settle in nursery areas (Able et al., 2005); areas that play a major role in the production of recruits (Beck et al., 2001) often with specific habitat characteristics. These are mainly depth, type of substrate and range in temperature and salinity, which favors growth and survival during the post-larval stage (Able et al., 2005). Accordingly, nursery areas vary in size and

Corresponding author. E-mail address: [email protected] (H.-H. Hinrichsen).

https://doi.org/10.1016/j.seares.2018.09.004 Received 23 January 2018; Received in revised form 15 August 2018; Accepted 7 September 2018 Available online 11 September 2018 1385-1101/ © 2018 Elsevier B.V. All rights reserved.

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stage (Nissling et al., 2002; Ustups et al., 2013; Petereit et al., 2014; Hinrichsen et al., 2017; Nissling et al., 2017) or during the juvenile stage after settlement in nursery areas (Aarnio et al., 1996; Nissling et al., 2007; Jokinen et al., 2016; Ustups et al., 2016). However, little attention has been paid to the pelagic larval stages being transported from the spawning areas to the nursery areas. Petereit et al. (2014) studied the drift of early flounder larvae in the western Baltic; the BeltSeas and Sound area (SD 22–23). The latter is known as the transition zone between the Kattegat and the Baltic Sea, but to the best of our knowledge no studies on flounder larval dispersal in the Baltic Sea (SD 24 through 32) exist. Hence, there is a considerable gap in knowledge of the life cycle of flounder in the Baltic Sea, potentially crucial for explaining recruitment variability in the year class strength. In an earlier study, variability in egg survival and dispersal up to the first feeding larval stage was evaluated for the flounder ecotype with pelagic eggs spawning in the Baltic offshore deep basins, using a hydrodynamic model (Hinrichsen et al., 2017). The model outcome suggested limited connectivity between the different spawning areas given the buoyancy of eggs and early yolk sac larvae in relation to topographic features. However, following hatching in the deep basins, the larvae migrate to upper water layers at the time of first feeding and are subjected to drift controlled by local atmospheric conditions and subsequent dispersal. This means that dispersal in later larval stages might show a different pattern affecting connectivity between flounder stocks spawning in different areas, i.e. between the two described Baltic flounder ecotypes (ICES, 2014; ICES, 2017). The objectives of this paper are i) to examine environmentally-related larval drift and bottom substrate-related settling probability (based on spatially highly resolved information on bottom substrate) of the flounder ecotype spawning pelagic eggs in the deep basins of the Baltic Sea,. ii) to estimate the potential for population connectivity between different spawning grounds of the central and eastern Baltic flounder by larval drift, and iii) to compare the habitat suitability for juvenile settlement with habitat occupancy of successfully settled juvenile flounder represented as drifting particles.

distribution depending on habitat preferences of the species. In flatfish, recruitment to the stock is governed by variability generated during the egg and larval stage and by density-dependent dampening after settlement, following the shift from a three-dimensional to a two-dimensional habitat (Iles and Beverton 1998; Van der Veer et al., 2000). van der Veer and Legget (2005) conclude that according to the “nursery size hypothesis”, recruitment in flatfish as a functional group is correlated with the approximate surface area of nursery grounds, explaining differences in recruitment both within and between species (Rijnsdorp et al., 1992; Van der Veer et al., 2000), i.e. larger nursery areas allow the potential for more larvae to survive to the juvenile stage. In strong settlement years lots of larvae could be delivered to all the different nursery grounds but only in the larger grounds there will be enough space and prey to allow the juveniles to survive. Moreover, according to the “supply side hypothesis”, stating that the abundance of later life stages depends on the number of larvae that successfully enter the nursery area, recruitment is determined by variability in the larval supply (Connell, 1985; Milicich et al., 1992). For example, Bolle et al. (2009) modeled egg and larval drift in North Sea plaice and provided evidence that the inter-annual variability in distance of the larval drift correlated with the observed variation in year class strength. Thus, both hypotheses highlight the transportation of larvae to suitable nursery habitats as a key mechanism for generating inter-annual variability in recruitment, circumstances that can be regarded as of particular significance for flatfishes with often specific nursery area preferences (Van der Veer et al., 1998; Bailey et al., 2005). Hence, variability in drift can be expected to result in year-class strength variability. In the Baltic Sea, flounder (Platichthys flesus) is the most common flatfish and an important target species for both commercial and recreational fishery. Two genetically distinct ecotypes (Hemmer-Hansen et al. 2007; Florin and Höglund 2008) or even species pairs (Momigliano et al., 2017, 2018) with different spawning strategies occur; one spawning pelagic eggs in offshore deep basins below the permanent halocline in spring and the other spawning demersal eggs in coastal areas and on offshore banks in spring-early summer (Nissling et al. 2002; Nissling et al. 2015; Ustups et al., 2013). The deep basin spawning ecotype mainly occurs in central and eastern Baltic Sea in ICES subdivisions (hereafter SD) 22–26 and in SD 28, and the coastal spawning ecotype mainly in northern Baltic Sea SD 27–30 and in SD 32 (Bagge, 1981; Nissling et al., 2002; Florin and Höglund, 2008; ICES, 2014; Fig. 1). Both ecotypes share feeding areas in coastal waters during summer-autumn but utilize different habitats for spawning in spring (Aro, 1989; Nissling et al., 2002), and moreover, utilize the same type of nursery areas, mainly shallow sandy bays (e.g. Florin et al., 2009; Martinsson and Nissling, 2011). Variations of larval drift to successfully end up in suitable nursery areas varies among species and populations within species, depending on the distance between spawning- and nursery areas (Bailey et al., 2005). Coastal spawning species may benefit from mechanisms favoring retention of larvae close to the spawning area whereas larvae of offshore spawners rely on transport to inshore habitats. Thus, larval dispersal of the demersal ecotype is expected to be limited due to the close proximity between spawning- and nursery areas. In contrast, larval drift patterns of the pelagic ecotype potentially involve wide-spread dispersal. Several studies have noticed fluctuating stock abundance of flounder in the Baltic Sea (Ojaveer et al., 1985; Drews, 1999) with a recent substantial decline in abundance in the northern areas (SD 29–30 and SD 32; Jokinen et al., 2016). According to the catch per unit effort (CPUE) within the Baltic International Trawl Surveys the stock has increased during the last decade in the central Baltic Sea (SD 24–25) but fluctuated considerably in eastern Baltic Sea (SD 26 and SD 28) with a substantial recent decline in SD 28 (Orio et al., 2017; ICES, 2017). Concerning the factors influencing recruitment variability, several studies have focused on either factors affecting survival during the egg

2. Material and methods 2.1. Hydrodynamic modeling The basis of the Lagrangian particle tracking is the hydrodynamic Kiel Baltic Sea Ice-Ocean Model (BSIOM, Lehmann & Hinrichsen, 2000; Lehmann et al., 2002). At present the horizontal resolution of the coupled sea-ice ocean model is 2.5 km and vertically 60 levels are specified with the upper 100 m resolved into levels of 3 m thickness. The model domain comprises the Baltic Sea, Kattegat and Skagerrak. At the western boundary, a simplified North Sea is connected to the model domain to represent characteristic North Sea water masses in terms of characteristic temperature and salinity profiles resulting from different forcing conditions. The model is forced by low frequency sea level variations in the North Sea/Skagerrak calculated from the BSI (Baltic Sea Index, Lehmann et al., 2002; Novotny et al., 2006). The coupled sea ice-ocean model is forced by realistic atmospheric conditions taken from the Swedish Meteorological and Hydrological Institute (SMHI Norrköping, Sweden) meteorological database (Lars Meuller, pers. comm.) which covers the whole Baltic drainage basin on a regular grid of 1 × 1° with a temporal increment of 3 h. The database consists of synoptic measurements that were interpolated on the regular grid with a two-dimensional optimum interpolation scheme. This database, which for modeling purposes was further interpolated onto the model grid, includes surface pressure, precipitation, cloudiness, air temperature and water vapor mixing ratio at 2 m height and geostrophic wind. Wind speed and direction at 10 m height were calculated from geostrophic winds with respect to different degrees of roughness on the open sea and off the coast (Bumke et al., 1998). BSIOM forcing functions, such as wind stress, radiation and heat fluxes were calculated 12

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Fig. 1. ICES subdivisions in the Baltic Sea, mean abundance (1971–2010) at release locations of hatched larvae (color scale) and Eastern and Central Baltic flounder nursery areas with sandy habitat distribution (grey scale).

according to Rudolph and Lehmann (2006). Additionally, river runoff was prescribed from a monthly mean runoff data set (Kronsell and Andersson, 2012). The numerical model BSIOM has been run for the period 1971–2010. This time series was used for the subsequent analysis of juvenile flounder habitat occupancy and successful early life stage transport to nursery grounds in the Baltic Sea.

Table 1 Available habitat size for settling larval (photic sand – bottom < 10 m). Probability ICES subdivision West of 24 24 25 26 27 28 29

2.2. Estimation of available Baltic flounder juvenile habitat The type of substrate is a major factor affecting the survival of juvenile flounder post settlement. As the juveniles prefer sandy habitat in shallow waters (Aarnio et al., 1996; Florin et al., 2009), highly spatially resolved substrate types (horizontal resolution of 2.5 km) based on model interpolation were taken from horizontal maps provided by the EU-BALANCE project (Al-Hamdani and Reker, 2007). They were used in order to calculate the area of available central and eastern juvenile Baltic flounder settlement habitat. The latter was performed separately for the ICES SDs 24 to 29 and for the Baltic Sea area west of the ICES SD 24. Due to limited computer capacity, this database was downscaled onto the model grid (resolution of 2.5 km) and includes six different bottom substrate types within each model grid box (photic sand, nonphotic sand, photic mud and clay, non-photic mud and clay, photic hard bottom, non-photic hard bottom). The horizontal distributions of habitat suitable for settlement of juvenile flounder as well as their sizes are illustrated in Fig. 1 and Table 1.

> 0–50% [km2] 2488 194 544 0 206 56 850

> 50–100% [km2] 5819 963 194 263 113 275 1700

2.3. Vertical distribution of larval flounder in the central and eastern Baltic To get information about the depth distribution of early larval stages, vertically resolving sampling gear was used during the spawning season of flounder in the Bornholm Basin (SD 25), Central Baltic Sea. On up to three annual cruises, one in April, one in May/June and one in August (no larvae caught, data not displayed) were conducted. The sampling covered deep areas (> 80 m; station BB23/BB22) and shallower regions (< 55 m; station BB32) (Fig. 2). The multi openingclosing net used was a HYDROBIOS MAXI-type net (Hydrobios, Germany) with a net mouth opening of 0.5 m2 equipped with 9 nets of 335 μm mesh size. In April 2012, the multi-net used was a HYDROBIOS MAMMUTH-type net (Hydrobios, Germany) with a net mouth opening of 1.0 m2 equipped with 9 nets of 335 μm mesh size. On the deep stations (BB22 & BB23) double hauls were performed with the multi-net 13

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Fig. 2. Proportions (%) of flounder larval abundances per depth strata as mean + standard deviation from different sampling years (2008–2012: RV ALKOR Aprilcruises AL318, AL371, AL390; May-cruises AL320, AL354, AL373) on different stations (> 80 m deep); Bottom depth is indicated by shaded area. The data from the shallow water sampling < 55 m (April AL390) represent mean and standard deviation of a double haul (replicated sampling). Single dashed lines visualize the 30 m depth layer used in the model as maximum release-depth of the model drifters.

2.4. Particle tracking model and estimation of availability of Baltic flounder juvenile habitat

integrated over the whole water column in 5 m depth layers from the surface to the bottom (n = 18 net for the whole water column). On the shallower station (BB32) the multi-net integrated over 10 m depth layers from surface to 20 m depth, and below 20 m also in 5 m depth layers down to the bottom. Towing time for each net was restricted to 3 min at a maximum speed of 3kn for each deployment. The samples were fixed with 4% formalin and were sorted and analyzed to species level after the cruises. As justification for the depth-selection (see below) for the modeling exercise, relative abundances of the larvae per depth layer are provided as means ( ± standard deviation) in three different years (Fig. 2). Depending on year, 95%–100% (mean 98%) of all larvae where sampled above 30 m during April cruises, and 90%–98% (mean 95%) during May/June cruises. On the shallow station in April 99% of the flounder larvae were sampled shallower than 30 m (Fig. 2).

Simulated three-dimensional temperature and velocity fields were extracted (at 3 h intervals) from the hydrodynamic model in order to develop a database for particle tracking. This data set offers the possibility to derive Lagrangian drift routes from Eulerian flow fields by calculating the advection of “marked” water particles. The three-dimensional trajectories of the simulated drifters were computed using a 4th order Runge-Kutta scheme (Hinrichsen et al., 1997), where larval and juvenile flounder were treated as simulated passively drifting particles.

2.5. Release position of drifters representing first feeding larvae A schematic flow diagram of Baltic flounder early life stage drift modeling from egg and yolk-sac larval stage (Hinrichsen et al., 2017) to the metamorphosed larval and juvenile stage (this study) is presented in 14

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Fig. 3. Schematic flow of Baltic flounder early life stage drift modeling (this study and Hinrichsen et al., 2017).

general information of abundance and length distribution of larvae and juveniles by depths zones, months and subdivisions. Due to the sampling design covering exclusively the pelagic part of the water column, very recently settled larvae could be underestimated in the samples. From preliminary analyses of this database two general results emerged: no larvae at the metamorphosis stage were sampled in areas with depth > 30 m and no small larvae (before metamorphosis) reached coastal areas. Thus, our model was run based on the following assumptions: in cases where metamorphosed larvae were located in areas with bottom depth < 30 m, they were considered able to reach sandy settlement habitats in shallow water areas as juveniles by swimming, whereas if settled at depth > 30 m they were regarded as unable to reach shallow water areas and were excluded.

Fig. 3. Larvae at the first feeding stage were released into the simulated flow fields and tracked through the model domain until time of settling. The initial horizontal distribution patterns of first feeding larval stage was taken from overall mean results obtained from the drift study on Central and Eastern Baltic flounder eggs performed by Hinrichsen et al., 2017 (their Fig. 8; Fig., 1 this study). Accordingly, for each single drift model run 2535 drifting particles were released within the historically important Baltic flounder deep basin spawning grounds (the Arkona Basin, the Bornholm Basin, the Gdansk Deep and the Gotland Basin; SDs 24, 25, 26 and 28) (Fig. 1) as first feeding larvae. Each larva was given a weight derived from the long-term spatial distribution (1971–2010) of survivors from the egg stage up to the first feeding yolk sac larval stage. Thus, the amount of drifters released is based on the spatial distribution of survivors in the flounder egg drift study and it is not based on a release of an equal number of drifters per hatching area or an equal density proportional to the size of the spawning areas. Drifting particles were released horizontally at depths between 0 and 30 m on a regularly spaced grid (2.5 × 2.5 km) as justified by field vertical distribution sampling (see section above). The positions of the drifters varied over time as a result of the three-dimensional velocities experienced. Based on hydrodynamic modeling, ambient temperature was recorded along the whole trajectories. Once a drifting particle reached the temperaturedependent metamorphosis stage (Hutchinson and Hawkins, 2004), it settled at the bottom at this position.

2.7. Early juvenile movement performance As a final step, to successfully find their suitable habitat for settlement, the juveniles (particles) need to swim towards shallow coastal areas actively and/or by benefitting from currents by appropriate larval behavior as discussed below. Using 1 body length/s swimming speed and an average reduction of 50% due to random non-directed feeding migrations, the possible swimming distance for 10–20 mm large larvae is between 0.43 and 0.86 km/day. Relationships obtained from lab experiments on larval and juvenile growth rates published by Hutchinson and Hawkins (2004; estimated from different temperaturedependent age-growth relationships presented in their Fig. 9) were used to obtain the developmental time from the metamorphosis stage (8 mm length) to a length of 15 to 20 mm at arrival in the nursery areas (Martinsson and Nissling, 2011). An estimated developmental time of about 30 to 45 days can be expected from the metamorphosis stage to the arrival in the nursery areas. Thus, it would take 23–46 days for the juveniles to cover a distance of about 20 km in order to find their final positions, a sandy habitat.

2.6. Settling likelihood at metamorphosis stage To get any information about the in situ occurrence of flounder larvae at the metamorphosis stage we made use of the BIOR data base (unpublished data; Institute of Food Safety, Animal Health and Environment BIOR, Daugavgrivas str 8, Riga LV-1048, Latvia). This database consists of > 5000 vertically resolved sampling stations and covered the time period from 1970 to 2006 (Ustups et al., 2013). Sampling was performed in the water column with an ichthyoplankton net with an opening of 0.5 m2 and mesh size of 500 μm. The database could of course not provide any information of the swimming ability of flounder juveniles to reach the coastal environment, but it provides

2.8. Distance and nursery habitat quality check Once the required habitat could be found by a juvenile within the above prescribed time period and distance, the location of settlement 15

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and the corresponding substrate type were recorded. Hence, in case the actively swimming particles were not able to find sandy habitat less than a 20 km swimming distance away, they were not counted as settled juveniles but excluded.

Table 3 Distance from location of juveniles at metamorphosis to closest location to suitable sandy settling habitat. Distance [km]

2.9. Spawning time and spawning areas 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–150

The focus of this study is on the general reproduction potential for flounder in the central and eastern Baltic Sea during the whole spawning season. Flounders are known to spawn from February to April in the Arkona Basin and Bornholm Basin (SDs 24 and 25), and from March to mid-June in the Gdansk Deep and Gotland Basin (SDs 26 and 28) (Bagge, 1981). In order to consider seasonal variability in relation to spatial and temporal variations in larval transport, drifting particles were inserted into the modeled flow fields at 10-day intervals, covering the period 10th February to 10th June, encompassing the historical as well as the present main spawning period of flounder (Bagge, 1981).

Frequency in ICES subdivisions [%] 24 & 25

26 & 28

62.68 26.16 7.00 2.24 1.17 0.11 0.04 0.60

52.86 21.06 11.70 9.99 2.99 0.10 0.01 1.27

grounds was observed to be dominated by settlement of particles west of subdivision 24 (~43%), i.e. outside the Baltic proper, and ~57% of the particles within the Baltic Sea (SD 24–29). The SD 24 contributed on average 15%, SD 25 20%, SD 26–13%, and SD 28 9% to the total number of successfully settled simulated juveniles. The number of particles which settled in the SDs 27 and 29 was negligible relative to the other areas. The fraction of dispersed particles which originally hatched in the Bornholm Basin (SD 25), which is the major spawning ground with ~72% of successfully settled larvae (Table 4), contributed on average 15% to SD 24 and ~37% to the area west of the latter whereas merely ~19% remained within SD 25, and with very few particles settling in SD 26 and 28.

3. Results 3.1. Distribution of larvae at the metamorphosis stage The share of particles at the metamorphosis stage at different distances from a sandy habitat is shown in Tables 2 and 3. The majority of simulated larvae, ~65–89%, settled at < 30 m depth occurred < 20 km from a suitable nursery habitat, whereas if settled at > 30 m depth only ~3–34% of the particles settled < 20 km from a suitable nursery habitat. This confirms our assumptions made regarding the settling likelihood of larvae at the metamorphosis stage (see Material and Methods). The majority of larvae ended up at ≤20 km from the preferred sandy habitat; ~89% of larvae hatched in the central Baltic Sea deep basins (SDs 24 and 25) and ~74% of those hatched in the eastern Baltic Sea deep basins (SDs 26 and 28). Because of the maximum swimming distance of 20 km, the locations of the metamorphosed larvae (Fig. 4) obtained from the model results suggest that larvae from the Arkona Basin and the Bornholm Basin (SDs 24 and 25) to a large extent would have their nursery areas along the coastal areas of SD 24, the Belt Seas and Sound SDs 22 and 23 and the Kattegat, Mainly they drift to the west, but may also to some extent use the coast of the southeast Baltic proper (SDs 25, 26 and 28). In contrast, larvae hatched in the Gdansk Deep and the Gotland Basin (SDs 26 and 28) remain in the Baltic proper with nursery areas mainly in SD 25, 26, 28 and southern SD 29.

3.3. Spatially disaggregated distribution patterns of simulated juvenile flounder Surviving (successfully settled) juveniles released as first feeding larvae in the different hatching areas (Fig. 5) show that most had hatched in the Bornholm Basin (SD 25). Compared to the Bornholm Basin, significantly different contributions of juveniles with origin in the Gdansk. Deep, the Gotland Basin and the Arkona Basin (SD 26, SD 28 and SD 24) are of less importance. Survivors were found, however, to be distributed in all SDs, because a significant amount was transported to regions outside the hatching areas. Highest successful settlement is located in the area west of the SD 24, followed by the SD 24 and 25. Low settlement was observed in the SD 26 and 28, which is similar to the low abundances of hatched larvae in these areas (Hinrichsen et al., 2017). The distribution patterns in both surviving juveniles released as first feeding larvae as well as successfully settled juveniles indicate a high annual variability in the area west of SD 24 and in the SDs 24 and 25. In contrast, the patterns indicate much lower variability in SDs 26 and 28.

3.2. Connectivity patterns of simulated larval and juvenile flounder To quantify the connectivity patterns of settled particles, we calculated their retention within as well as their dispersal between SDs (Table 4). In a long-term perspective, successful transport to nursery

Table 2 Frequency of distances from locations with different bottom depths to nursery areas [%], values marked in bold represent the share of particles that will be able to actively swim to suitable nursery habitat. Bottom depth [m]

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100

Distance to nursery areas [km] 0–10

10–20

20–30

30–40

40–50

50–60

60–70

70–80

80–90

> 90

81.56 61.87 24.60 6.80 2.40 1.16 0.53 0.46 0.50 0.32

7.16 24.94 39.92 26.95 16.89 11.19 6.84 5.93 2.29 2.72

3.17 5.98 19.54 26.51 27.60 16.53 15.34 13.56 6.77 12.07

1.51 1.90 8.00 15.06 22.92 20.81 15.87 16.24 9.02 14.54

0.64 1.05 2.12 5.57 10.46 16.28 19.72 14.18 16.67 17.95

0.70 0.61 0.56 3.70 4.79 7.02 8.55 16.13 19.24 12.83

0.90 0.25 1.48 2.12 1.75 5.82 7.16 12.63 14.67 9.73

1.08 0.32 1.44 2.12 2.88 5.30 7.38 7.42 10.31 12.77

0.76 0.32 0.90 3.64 4.12 4.46 6.15 4.18 6.73 6.01

2.53 2.76 1.44 7.53 6.20 11.44 12.45 9.28 13.80 11.06

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Fig. 4. Horizontal distribution of log10-transformed numbers of drifter per 8 km2 representing successfully settled 0-group flounders at their metamorphosis larval stage originated in ICES SDs 24 (A), SD25 (B), SD26 (C), and SD28 (D). Table 4 Overall means and confidence interval (95%) of dispersal/retention patterns (percentages) of virtual drifters representing settled Baltic flounder juveniles after application of arcsine transformation. Bold numbers represent retention patterns within ICES subdivisions. West means ICES SDs further west than ICES SD 24. Spawning area

SD SD SD SD

24 25 26 28

SD SD SD SD

24 25 26 28

Drifter location at settled juvenile stage West

SD 24

SD 25

SD 26

6.93 ± 0.83 36.42 ± 4.65 0.0 0.0 SD 27 0.0 < 0.01 0.0 0.0

0.33 ± 0.07 12.50 ± 2.10 0.0 0.0 SD 28 0.0 < 0.01 3.69 ± 1.22 4.62 ± 1.28

0.18 ± 0.04 19.76 ± 2.92 1.62 ± 0.79 0.01 ± 0.01 SD 29 0.0 0.0 0.06 ± 0.06 0.54 ± 0.27

0.0 2.25 ± 0.74 11.14 ± 1.31 0.49 ± 0.26

Fig. 5. Boxplots of annually averaged numbers of virtual drifters representing successfully settled Baltic flounder juveniles (survivors) west of the Arkona Basin (ICES SD 24), hatched and successfully settled in the Arkona Basin (ICES SD 24), Bornholm Basin (ICES SD 25), Gdansk Deep (ICES SD 26), and Gotland Basin (ICES SD 28).

28, 75% of the available habitat (Table 3) was finally occupied each year by settled juvenile drifters. In SD 24 and in the area west of this subdivision the relative mean occupied habitat was around 50%, while in SDs 27 and 29 very low proportions of available habitat were occupied. Furthermore, the density of successfully settled simulated flounder juveniles in the different SDs, i.e. the number of juveniles per km2 which finally arrived in a suitable habitat is shown in Fig. 7.

3.4. Spatially disaggregated distribution patterns of simulated occupied juvenile habitat The long-term means of the relative occupied juvenile flounder habitat within the different SDs are shown in Fig. 6. In SDs 25, 26 and 17

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knowledge about biological interactions for this species (predation, feeding competition, match-mistmatch of predator and prey). Furthermore, the results obtained from biophysical modeling are based on equal mortality between areas during larval drift. Thus, successful transport of larval/juveniles to the nursery grounds could only be considered as probability because they could not be tested with field data on the distribution of settled flounder. In the Baltic Sea the majority of studies focusing on 0-group flounder ecology have been directed to sandy habitat nursery areas (Kostrzewska-Szlakowska and Szlakowski, 1990; Aarnio et al., 1996; Ustups et al., 2007; Nissling et al., 2007; Jokinen et al., 2016), which has been shown to be the preferred habitat (Florin et al., 2009). The present study suggests that the majority of larvae drifted towards coastal areas within 20 km distance from the preferred sandy habitat. On average the majority of larvae end up in suitable habitat. But the variability as shown in Fig. 5 suggests that larval drift could be a major bottleneck in recruitment for deep basin spawning flounder in the Baltic Sea, with in some good years 3 times higher settlement probability compared to poor years. The drift model suggests that flounder hatching in the Arkona Basin and Bornholm Basin (SDs 24 and 25; central Baltic) utilize nursery areas in mainly the SDs 22–25 and the Kattegat, whereas flounder hatching in the Gdansk Deep and Gotland Basin (SDs 26 and 28; eastern Baltic) mainly utilize the southern and eastern coast of the SDs 25, 26 and 28 (Fig. 5; Table 4). Thus, the two stock components, regarded as separate subpopulations (ICES, 2014; ICES, 2017; Nissling et al., 2017), seem to use different areas following settlement. Studies on both common sole and plaice support the nursery size hypothesis as well as the supply side hypothesis (Rijnsdorp et al., 1985, 1992; Bergman et al., 1989; Van der Veer et al., 2000) that larger nursery areas generally could potentially support more settling larvae during their later stages. The present study shows that the highest occurrence of the preferred nursery habitat is found in the area west of SD 24, i.e. outside the Baltic proper (5819 km2), followed by SD 24 (963 km2) but with lower occurrence in the main spawning areas (SD 25, 26 and 28; 194–275 km2). As evaluated from the model extensive sandy habitats occur also in SD 29 although here they were used only to a limited extent. Accordingly, 43% of the particles settled in the area west of SD 24 and 15% within SD 24 although the number of hatched larvae was low in this area. Concerning the most productive hatching area, the Bornholm Basin (SD 25) only 19% of the particles remained in the area; the majority drifted towards the west and settled in the area west of SD 24 and in SD 24. The areas of suitable habitat are limited in size in SD 25, 26 and 28. However, the fractions of occupied habitat were high whereas lower relative occupancy occurred in SD 24 and west of SD 24 in the Belt Seas/Oresund and the Kattegat with relatively large areas of suitable habitat. Only few particles settled in SD 28 and almost none in SD 27 and 29. In these areas however the flounder ecotype spawning in coastal areas (with demersal eggs) probably dominates (Florin & Höglund 2008, Hemmer-Hansen et al., 2007, Florin et al. in prep.). The highest relative survival from the egg to the yolk-sac larval stage occurred in the Gdansk deep (SD 26), and in the Bornholm basin (SD 25), with ca 40% of the total contribution of the survivors in each area (Hinrichsen et al., 2017). However, during the larval stage up to settling, this changed to ca 72% contribution of survivors from the Bornholm Basin and merely ca 15% from the Gdansk Deep (present study). This is in accordance with the “nursery size hypothesis” that the transport of larvae/juveniles from SD 25 to the large nursery habitat in the western Baltic (west of SD 24) allows more larvae to survive to the juvenile stage, whereas low abundances of larvae originating from the SD 26 and 28 settle mainly in areas with lower occurrence of suitable nursery areas, SDs 25, 26 and 28 (Fig. 2; Table 1). Both the amount of settled juveniles and density of settled juveniles as shown in Fig. 5 and Fig. 8, respectively, revealed high and relatively stable larval supply sustaining the stock for the Bornholm Basin (SD

Fig. 6. Boxplots of annually averaged relative available Baltic flounder juvenile habitat west of the Arkona Basin (ICES SD 24), in the Arkona Basin (ICES SD 24), Bornholm Basin (ICES SD 25), Gdansk Deep (ICES SD 26), in the eastern (ICES SD 27), in the central Gotland Basin (ICES SD 28), and in the northern Gotland Basin (ICES SD 29).

Fig. 7. Boxplots of annually averaged density of Baltic flounder juveniles [ind./ km2] west of the Arkona Basin (ICES SD 24), in the Arkona Basin (ICES SD 24), Bornholm Basin (ICES SD 25), Gdansk Deep (ICES SD 26), in the eastern (ICES SD 27), in the central Gotland Basin (ICES SD 28), and in the northern Gotland Basin (ICES SD 29).

The spatial pattern shows that the density of settled particles in the SDs 27, 29 and in the area west of SD 24 is relatively low (~104/km/2) compared to the SDs 24, 25, 26 and 28 (~105/km2). The inter-annual variability was found high in SDs 25, 26 and 28 and significantly lower in the area west of SD 24, and the SDs 24, 27 and 29. For more specific results on spatially disaggregated distribution patterns of simulated juvenile flounder and of simulated occupied juvenile habitat see Supplementary Material (Figs. SM1-SM4). 4. Discussion Variability in fish populations is driven by processes which operate on a variety of spatial and temporal scales. According to Van der Veer et al. (1998), for flatfish in general inter-annual variability in dispersal is quite large suggesting that variability in drift patterns is a key factor determining recruitment. This was confirmed by Bolle et al. (2009), who showed this inference with a hydrodynamic model for the southern North Sea driven by real time meteorological forcing. Conceptual models regarding Central and Eastern Baltic flounder recruitment success are mainly related to the impact of abiotic and biotic environmental variables. Generally, they tend to emphasize the time domain while under-representing the spatial dynamics (Ustups et al., 2013; ICES, 2017). Biophysical models are useful tools to explore the effects of variability in physical factors on key species (Hinrichsen et al., 2011) and could reveal the ecosystem elements that should be the focus of experimental and observational work (Hinrichsen et al., 2012). Although the physical conditions which reflect the influence of weather and climate conditions in the Baltic Sea are relatively well known, the most important present problem to develop a biophysical model for Central and Eastern Baltic flounder larvae/juveniles is the current lack of 18

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considering different biotic factors such as e.g. predation and starvation mortality as well as geographical differences of such factors. The connectivity between the nurseries west of the major spawning areas and the spawning stock, i.e. to what extent juveniles in these areas recruit to the spawning stock, is unknown but natal homing is a wellknown phenomenon (Ruzzante et al., 2006; Svedäng et al., 2007). As the present study suggests that drift to nursery areas west of the central Baltic Sea spawning areas occur regularly, a corresponding eastward migration when (at the latest) the fish reach maturity can be expected.

25), the major spawning area, but both lower and more variable recruitment for the Gdansk Deep (SD 26) and in particular the Gotland Basin (SD 28). This is in line with data on stock development (Orio et al., 2017) showing an increase in stock size in SD 24–25 and more fluctuating stock in SD 26 and in SD 28 with a decline recently. The results reflect probably habitat suitability for egg survival in the respective area. More stable salinity and oxygen conditions in the Bornholm Basin occurred compared to in the Gdansk Deep and in particular the Gotland Basin (Nissling et al., 2002; Ustups et al., 2013; Hinrichsen et al., 2017; Nissling et al., 2017) due to a lack of major saline inflow events during the last decades, perhaps an effect of climate change (MacKenzie et al., 2007; Meier et al., 2012). This emphasizes the importance of the spawning habitat in the life-cycle of Central and Eastern Baltic Sea flounder. Arrival to a nursery area is probably not a random process; flatfish larvae may maximize their probability to end up in a suitable nursery habitat following settling. Previous modeling studies indicate that vertical migration may result in a significant departure from passive particle drift (see Bailey et al., 2005; Sentchev and Korotenko, 2007). Several studies suggest both inter- and intra-specific adaptation in larval behavior to local conditions to favor retention in order to settle in a suitable nursery habitat (see Burke et al., 1998; Jager, 1999; Fox et al., 2006; Teodósio et al., 2016). In the North Sea with strong tidal currents, flounder larvae have been found within the water column during flood and in the bottom layer during ebb promoting shoreward drift and subsequent propagation into the Elbe River estuary (Boss et al., 1995). However, in the Baltic Sea tidal currents are insignificant so we only focused on the vertical distribution of larvae (Fig. 3) and their passive transport. Based on values of swimming speed in Blaxter et al. (1986), temperature-dependent development (Hutchinson and Hawkins, 2004) and data on days from hatching to settling (56 ± 13 days from otolith readings; Martinsson and Nissling, unpublished) a settled larvae should be able to migrate a distance of ca 20 km within a time period of 10 to 20 days. For flounder, the depth of first settling during metamorphosis is unknown. However, according to observations on plaice, larvae seem to settle “well offshore” during the stage of metamorphosis and then swim or are transported by currents to the nursery areas where only fully metamorphosed juveniles are found (Lockwood, 1974). Hence, in the present study we assume that first settling of larvae at < 30 m depth is necessary in accordance with the vertical distribution as well as with the assumed maximum traveling distance (< 20 km) corresponding to the developmental time from the metamorphosis stage to arrival in the nursery areas. The extensive dispersal during the larval stage in many flatfish species, potentially causing high gene flow, might be the reason for the small degree of genetic population structure among flatfishes studied to date in e.g. the North-East Atlantic (Bailey, 1997; Exadactylos et al., 1998; Hoarau et al., 2004), although a recent study applying new genetic methods revealed regional local adaptations under high level of gene flow in sole populations (Diopere et al., 2018). The present study revealed extensive larval drift from the main spawning areas in the central Baltic Sea towards the west with settling in the Belt Sea- and Kattegat area and even in the Skagerrak. This is in agreement with low genetic differentiation between flounder from the North Sea, the Belt Sea area and the central Baltic (Hemmer-Hansen et al., 2007; Florin and Höglund, 2008), separated from flounder in the eastern and northern Baltic Sea, i.e. including the coastal spawning ecotype. Hemmer-Hansen et al. (2007) concluded that in flounder the gene flow between the Atlantic and the western-central Baltic Sea is relatively high in comparison to other species in the region. The good accordance of our modeling results with genetic information of the flounder stock clearly identified the suitability of the 3-D hydrodynamic model for examining the circulation and the larval and juvenile transport of this species. However, the simulated particle end positions could only provide a general pattern compared to genetic information. The simulation provides distribution patterns at the larval stage without

5. Summary and conclusion The model suggests: 1. The majority of flounder larvae hatched from pelagic eggs end up in a preferred habitat. 2. Larvae originating from the central Baltic stock component (SD 24–25) utilize nursery areas in SD 22–25 and the Kattegat whereas the stock component in the eastern Baltic uses areas in SD 25, 26 and 28. 3. This is in agreement with relatively high gene flow between the Atlantic and western-central Baltic Sea separated from the easternnorthern Baltic Sea described previously in genetic studies on Baltic Sea flounder. 4. In accordance with the “nursery size hypothesis”, larvae from the Bornholm Basin (SD 25), utilizing areas with extensive distribution of preferred nursery habitat, display the highest successful transport of larvae up to settling in contrast to larvae from the main spawning areas of deep basin spawning flounder. Acknowledgements This work resulted from the BONUS INSPIRE and BIO-C3 projects and was supported by BONUS (Art 185), funded jointly by the EU and the Forschungszentrum Jülich Beteiligungsgesellschaft mbH (Germany), Grant No: 03F9682A, the Swedish Research Council Formas, Sweden and from the Latvian Academy of Science, Latvia. CP received funding through the “Egg density project” by DTU-Aqua, Denmark to GEOMAR. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.seares.2018.09.004. References Aarnio, K., Bonsdorff, E., Rosenback, N., 1996. Food and feeding habits of juvenile flounder Platichthys flesus (L.) and turbot Scophthalmus maximus (L.) in the Åland archipelago, northern Baltic Sea. J. Sea Res. 36, 311–320. Able, K.W., Neuman, M.J., Wennhage, H., 2005. Ecology of juvenile and adult stages of flatfishes: distribution and dynamics of habitat associations. In: Gibson, R.N. (Ed.), Flatfishes, Biology and Exploitation. Fish and Aquatic Resources Series, vol. 9. Blackwell Publishing, U.K, pp. 164–184. Al-Hamdani, Z., Reker, J., 2007. Towards Marine Landscapes in the Baltic Sea. BALANCE interim report #10 ISBN: 978-87-7871-203-5. Aro, E., 1989. A review of fish migration patterns in the Baltic. Rapports et ProcésVerbaux des Réunions du Conseil International pour l'Exploration de la Mer 190, 72–96. Bagge, O., 1981. Demersal fishes. In: Voipio, A. (Ed.), The Baltic Sea. Elsevier Oceanographic Series Vol. 30. Elsevier Scientific Company, Amsterdam, pp. 320–323. Bailey, K.M., 1997. Structural dynamics and ecology of flatfish populations. J. Sea Res. 37, 269–280. Bailey, K.M., Nakata, H., van der Veer, H.W., 2005. The Planktonic Stages of Flatfishes: Physical and Biological Interactions in Transport Processes. In: Gibson, R.N. (Ed.), Flatfishes, Biology and Exploitation. Fish and Aquatic Resources Series Vol. 9. Blackwell Publishing, U.K, pp. 94–119. Beck, M.W., Heck, K.L., Able, K.W., et al., 2001. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. Bioscience 51, 633–641. Bergman, M.J.N., Van der Veer, H.W., Stam, A., Zuidema, D., 1989. Transport mechanisms of larval plaice (Pleuronectes platessa L.) from the coastal zone into the Wadden

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