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Estuaries and Coasts

, Volume 41, Issue 1, pp 1–12 | Cite as

Storm-Induced Atlantic Herring (Clupea harengus) Egg Mortality in Baltic Sea Inshore Spawning Areas

  • Dorothee Moll
  • Paul Kotterba
  • Lena von Nordheim
  • Patrick Polte
Article

Abstract

During their spring migration, Atlantic herring (Clupea harengus) populations in the Baltic Sea rely on shallow transitional waters, such as estuaries, bays, and lagoons for spawning. Such inshore spawning grounds are ecologically important by providing suitable substrates for demersal egg deposition. These habitats are often highly impacted by multiple anthropogenic threats. Decades of eutrophication have caused a decline in depth distribution of submerged aquatic vegetation, the main herring spawning substrate in the Baltic Sea. Nowadays, spawning beds are limited to the shallow littoral zone (≤3 m depth). Accordingly, macrophytes are increasingly exposed to mechanic forcing due to storm-induced wave action. Generally, reproductive success and year class strength of the Western Baltic herring population is strongly determined by the survival of early life stages such as eggs and larvae in local nursery areas. However, explicit mechanisms by which local stressors might affect overall recruitment are currently not well understood. Hypothesizing that aquatic vegetation limited by water depth causes high herring egg mortality due to increased exposure to storm-induced hydrodynamics, we performed a combination of field studies investigating the impact of storm events on herring egg loss. Results of an egg loss experiment revealed a total egg loss of 29% in one single spawning bed during a storm event within the spawning season and the quantification of eggs attached to macrophyte litter on the shoreline emphasize the potential for regional weather extremes such as storm events to act as influential stressors for herring reproduction.

Keywords

Atlantic herring Clupea harengus Baltic Sea Storm effects Egg loss Submerged aquatic vegetation 

Introduction

A widespread reproduction strategy among fish species in marine and fresh water habitats is to spawn immense numbers of eggs to compensate for high mortality rates in the early life stages (McGurk 1986; Morrison et al. 1991).

The Atlantic herring (Clupea harengus, Linnaeus 1758) is probably one of the best studied fish species in the world and of high economically (FAO 2014) and ecologically importance (Cardinale and Arrhenius 2000; Casini et al. 2004; Möllmann et al. 2004; Overholtz and Link 2007). C. harengus follows this strategy by releasing large number of eggs in a short period of time during each breeding season (Murua and Saborido-Rey 2003; Wootton 1990). Additionally, herring is a “bet-hedging” strategist, where spawning stocks spread spawning activity in waves over time, increasing the chances of successful reproduction (Lambert 1990; Lambert and Ware 1984). This reproductive effort is required to ensure recruitment and therefore the survival of populations (Duarte and Alcaraz 1989).

C. harengus is a litho-phytophilous spawner (Balon 1975), attaching adhesive eggs either in deeper zones on outer coastal shelf gravel beds (North Sea populations) (Hempel 1971; Hempel and Schubert 1968) or to submerged aquatic vegetation (SAV) in the near shore zone (Baltic Sea population) (Aneer 1989; Aneer et al. 1983; Kanstinger et al. 2016; Klinkhardt 1986; Kotterba et al. 2014; Rajasilta et al. 1989; Scabell 1988). In contrast, other economically important fish species in the Baltic Sea region, such as Atlantic cod (Gadus morhua, L. 1758) and sprat (Sprattus sprattus, L. 1758) spawn pelagic eggs. While embryonic development and hatching success of passively floating, pelagic eggs is mainly dependent on hydrodynamic drift patterns, hydrographical conditions, and the physical properties of the eggs (Nissling et al. 2003; Petereit et al. 2014; Sundby 1991), the development of stationary benthic eggs is subject to local climate variability and spawning site-specific drivers and stressors. Since Johan Hjort’s “critical period” hypothesis postulated that the year class strength of a population is determined in the early larval stage during the transition between yolk consumption and active feeding (Hjort 1914), most research on herring early life stage ecology has basically focused on survival bottlenecks in the larval stage (Gröger et al. 2010; Urho 1999). However, quantitative studies on ecological drivers of egg mortality in natural spawning beds are scarce and limited to the effect of toxic exudates of algae species (Aneer 1987; Rajasilta et al. 2006) and physico-chemical variables such as changes in water temperatures and salinity (Alderdice and Velsen 1971; Blaxter 1956; Peck et al. 2012). Furthermore, oxygen depletion (Braum 1973; Klinkhardt 1986) and pollutants (Kinne and Rosenthal 1967; Ojaveer et al. 1980; Rosenthal and Sperling 1974; Von Westernhagen et al. 1979) show that these external stressors can highly affect herring egg mortality.

Even though high egg mortalities can be a natural consequence of excessive spawning, there is evidence that increased egg mortality can affect population dynamics, e.g., through egg predation by other fish species (Kotterba et al. 2014; Richardson et al. 2011) or by seabirds (Haegele 1993; Haegele and Schweigert 1989; Rooper et al. 1999). Although recent studies from the Pacific and Atlantic region provide the hypotheses that herring recruitment occurs mainly during the juvenile life stage (Bishop et al. 2015; Foy and Paul 1999; Sætre et al. 2002), there is ample evidence for Western Baltic herring recruitment bottlenecks predominantly located in the early life stages previous to metamorphosis (Cushing 1975; Hjort 1914; Houde 2008; Oeberst et al. 2009; Polte et al. 2014). Egg survival and even recruitment of populations often seem to be determined on the regional scale of important spawning grounds. Thereby, actual impacts of regional drivers depend on the timing and coincidence with critical developmental periods of earlier herring stages. Polte et al. (2014) reported distinct hatching peaks along the season with differing contributions to the recruitment of the Western Baltic spring spawning herring stock and indicated differing cohort-specific, early-stage survival bottlenecks. They considered egg mortality as a potential driver of the particular cohort that correlated the most strongly with recruitment success.

Besides changes in environmental factors and predation effects, storm events and related effects in hydrodynamic forcing can potentially lead to high egg mortalities. However, there is limited information on the subject of herring egg loss due to wind-induced hydrodynamics (Haegele and Schweigert 1989; Hart and Tester 1934; Hay and Miller 1982; Rooper 1996; Tester and Stevenson 1948). Most of the existing studies have been conducted in the vicinity of Vancouver Island, British Columbia, Canada, where Pacific herring (Clupea pallasii) deposit demersal eggs on eelgrass (Zostera marina), rockweed (Fucus evanescens), and other brown algae (Taylor 1964) and therefore shows a similar spawning mode to spring spawning C. harengus in the Baltic Sea. The above studies in the coastal Pacific have indicated a high impact of storm activity on herring egg mortality causing egg losses of up to 25% of the total amount of eggs in the study area, corresponding to a spawning capacity loss of 576 t (Hay and Miller 1982).

Since the middle of the last century, eutrophication processes have severely affected the depth distribution of SAV in the inner coastal waters of the Baltic Sea by increased water turbidity, and the correspondingly reduced light availability for aquatic macrophytes (Kanstinger 2014; Messner and Von Oertzen 1990; Munkes 2005b). Consequently, depth distribution of SAV, the main spawning substrate for Baltic herring populations, has decreased significantly and is nowadays limited to the shallow near shore zone (Geisel 1986; Messner and Von Oertzen 1991; Schiewer 2001). Hence, these remaining beds of suitable spawning substrate are increasingly exposed to hydrodynamic forcing. Noting that climate change science forecasts overall increased storm frequency and intensity (Coumou and Rahmstorf 2012; Woth et al. 2006), it is important to know the impact of single storm events on herring egg mortality.

To investigate the impact of local storm events on herring egg mortality, we combined field experiments in an important herring spawning ground with quantitative herring egg sampling and estimated consequences of egg loss on herring recruitment. The present study addresses the hypotheses that (i) depth distribution of eggs on different vegetated spawning zones affects the magnitude of storm-induced egg mortality and (ii) local storm events significantly affect herring egg mortality.

Methods

Study Area

The study area Greifswald Bay is a semi-enclosed brackish and eutrophic lagoon (514 km2) with a mean salinity of 7.3 PSU (Kell 1989). The bay is formed by the island of Rügen northwards and the German mainland in the south. Greifswald Bay is known as a major spawning area for Western Baltic spring spawning herring (Klinkhardt 1984; Scabell 1988). It is a shallow water body with a mean water depth of 5.8 m (with max. depth of 13.5 m), connected with the Baltic Sea by a narrow sound (Strelasund) westward and a wide, but shallow opening in eastern direction (Fig. 1). The specific topography of the lagoon results in a semi-enclosed basin structure leading to a restricted water exchange with the adjacent waters with a water exchange rate ranges from eight times (Stigge 1989) to almost 12 times a year (Schiewer 2008). Since tides are rather marginal, sea level amplitudes and water exchange processes are mainly wind driven (Schnese 1973). The water body is generally well-oxygenated due to wind mixing events (Schiewer 2008), and seasonal fluctuation in water temperatures is highly influenced by atmospheric temperature regimes.
Fig. 1

The investigated herring spawning site in Greifswald Bay (middle) and its location within the Western Baltic Sea (top). Dots in the aerial image of the spawning bed (lower map) indicate the location of fixed transects used for the monitoring of depth-dependent spawning intensity. The square represents the area used for the storm impact experiment, while the triangles on the beach indicate the sampling points for the examination of storm-induced SAV litter. The broken line defines the area that represents spawning zone “A”, and the dotted line limits the spawning zone “B”, while spawning zone “C” is indicated by the solid line. Black areas on the upper corner of the aerial image indicate SAV-free zones with water depth of more than 5 m

The study site “Gahlkower Haken” is an important spawning ground for herring (Scabell 1988), located at the southern coast of Greifswald Bay (Fig. 1). The area is characterized by extended beds of SAV. These spawning beds are located in the shallow littoral zone and are stratified by depth with growth limits at a maximum water depth of approximately 3.5 m for Spermatophytes, due to an eutrophication-induced increase in water turbidity (Kanstinger 2014; Munkes 2005a). The “pondweed” zone in the very shallow area comprises flowering plants of the families Potamogetonaceae and Ruppiaceae, in addition to a diverse community of other flowering plants and filamentous algae. The subsequent seagrass zone is dominated by Zostera marina (Geisel and Messner 1989).

Field Sampling

Herring Egg and SAV Sampling

To investigate herring egg depth distribution and SAV composition, we defined three permanent transects located parallel to the shoreline of the spawning area Gahlkower Haken covering distinct depth zones with corresponding differing SAV compositions. Transect “A” (Fig. 1) is characterized by 1 m water depth comprising the pondweed zone, transect “B” is a transitional zone with mixed stands of pondweed and seagrass (depth ca. 1.5 m), and transect “C” is characterized as the seagrass zone, about 2–3 m deep (Fig. 1). Each transect consisted of six sampling stations in a row (distance between neighboring stations 25 m, total transect length 125 m). Herring spawning activity started in mid-March and lasted until the middle of May in 2012. To investigate herring egg concentrations during the entire spawning season, weekly egg sampling was conducted at the six stations on each transect. The samples were taken with a small van Veen grabber (sampling area = 400 cm2) which has performed well on the soft bottom of the spawning bed in previous trials and provided comparability of results to earlier studies (e.g. Kotterba 2015).

The Pre-impact/Post-impact Experiment

To quantify the storm-induced loss of SAV biomass and equivalent herring egg loss, we conducted a field experiment on the particular depth strata of transect A (1 m depth; Fig. 1), where significant egg depositions were observed in earlier years. Experimental units included defined spots homogeneously covered with SAV that were marked with six tags on the sea bottom to compensate for potentially high data variability of egg concentrations on natural spawning beds according to SAV patchiness. Prior to and after a predicted storm event (end of March–early April), experimental plots were sampled with a directed Van Veen grab led by a snorkeler on differing sides of the tag.

Characterization of Storm Events

Hourly wind data of the weather station “Greifswalder Oie” (N 54.249043°, E 013.923850°) was provided by the Federal Meteorological Service of Germany (DWD). The daily maximum wind speeds (m s−1) and the corresponding wind directions during the spawning period were extracted from the time series of weather recordings for the study area.

Post-storm Beach Litter Sampling

After a multiple-day storm event, a 571 m long beach section on the lee side of the spawning ground (Fig. 1) was sampled for SAV litter with attached herring eggs. This was performed to identify the spawning zones most affected by the storm according to SAV composition and to verify numbers of herring eggs exported out of the system. The total washed up SAV biomass in gram fresh weight (g FW) was determined on 0.5 m wide transects spanning the entire beach width (delineated from edge of dune vegetation landward to the water level). Sixteen of those transects were sampled approximately every 40 m (more details in Online Resource 1), and after recording the total gram FW of SAV per transect, a random subsample of approximately 1 l of SAV litter was fixed with a 4% formalin-seawater solution and transferred to the laboratory to analyze SAV composition, to count the number of herring eggs and to determine their condition.

Sample Processing

In the laboratory, fixed samples were rinsed for at least 24 h in cold water to remove the fixative. The total plant biomass in gram fresh weight (g FW) of each sample was recorded, and percentage of taxonomic plant composition was identified on the family level. For exact analyses of egg quantity and egg condition, each sample was spread on a tray, three subsamples of standardized area were taken randomly (Online Resource 2), and SAV subsample biomass (g FW) was recorded. Then, eggs were separated from the plants under a stereo microscope. Eggs were counted according to different categories (alive and dead, more details in Online Resource 3), and biomass (g FW) of all eggs was recorded using a micro scale. Afterwards, all plant and egg samples were dried at 80 °C for at least 48 h in a compartment drier for recording dry weight. SAV biomass in gram dry weight (g DW) and the number of eggs on the three different depth zones were extrapolated to square meter and were quantified for the entire spawning period in 2012.

Determination of Total Spawning Zone Surface Area

A Geographic Information System (ArcGIS, 2013 Esri, Arc Map version 10.2) was used to determine the total area of each vegetation covered spawning zone at the study site Gahlkower Haken. Area definition was performed by manually delineating the vegetated zones, using an aerial image analysis and the GIS tool to measure distance (Fig. 1).

Data Analyses

Estimation of Total SAV Biomass per square meter and Total Egg Number per square meter

To cope for high fluctuations in fresh weights (g FW), we used dry weights (g DW) to extrapolate the SAV biomass (g m−2) for all samples according to the following equation:
$$ {DW}_{SAV}=\left(\frac{DW_{SAV ss}}{DW_{\left( SAV+ HE\right) ss}}\times {DW}_{RS}+{DW}_{SAV ss}\right)\times \chi $$
(1)
where DW SAV represents the total SAV dry weight per square meter for each replicate, DW SAV ss is the dry weight of SAV in the subsample and DW (SAV+HE) ss is the dry weight of the entire subsample (including SAV and attached herring eggs), and DW RS is the dry weight of the remaining sample (subsample excluded). x represents the factor needed to extrapolate the sampling area of the grabber to square meter (e.g. x = 25, if the sampling area was 400 cm2). For calculating the total amount of herring eggs per square meter for each replicate, we used the following equation
$$ {n}_{HE}={DW}_{SAV}\times \frac{n_{HE ss}}{DW_{SAV ss}} $$
(2)

where n HE is the number of eggs per square meter, DW SAV represents the total SAV dry weight per square meter, n HE ss represents the number of eggs, and DW SAV ss is the SAV dry weight in the subsample.

Calculation of Egg Numbers per Spawning Zone

The egg numbers per square meter (n HE ) were multiplied with the surface area of each spawning zone A SZ to estimate the mean number of eggs for each spawning zone (\( \overline{n_{HE\; sz}} \)):
$$ \overline{n_{HE\; sz}}={n}_{HE}\times {A}_{sz} $$
(3)

Loss of Eggs as Shown by the Pre-impact/Post-impact Experiment

We analyzed the storm-induced observed egg loss per square meter (EL Obs ) on each experimental unit subtracting the egg number after the storm (n HE a ) from the egg number prior to storm (n HE p ). The EL Obs is interpreted as the maximum egg loss occurring during the storm event:
$$ {EL}_{Obs}=\left({n}_{HE\; p}-{n}_{HE\; a}\right) $$
(4)

Following the assumption that adherent herring egg abundances correlate strongly to the suitable spawning substrate (Kanstinger et al. 2016), we assumed that any loss of SAV (e.g. due to storm events) directly results in a loss of adherent herring eggs, considering this as the minimum egg loss. We conducted a pairwise pre-storm/post-storm comparison for each replicate to determine the minimum egg loss EL SAV , using the following equation:

$$ {EL}_{SAV}=\left(\frac{DW_{SAV\; a}}{DW_{SAV\; p}}\right)\times {n}_{HE\; p} $$
(5)

where n HE p is the egg number at the time prior to the storm DW SAV p is the dry weight of SAV prior, and DW SAV a is the dry weight SAV after the storm event.

Extrapolation of SAV Litter Biomass

The SAV biomass (g DW) found on 1 m beach width (DW Beach ) was extrapolated using the following equation:

$$ {DW}_{Beach}=\left(\frac{DW_{SAV\; ss}}{DW_{\left( SAV+ HE\right)\; ss}}\times {DW}_{RS}+{DW}_{SAV\; ss}\right)\times f\times 2 $$
(6)

where f represents the factor needed to extrapolate the sample to the total biomass of SAV litter on the corresponding beach section of each transect (total fresh weight of SAV litter per transect divided by the fresh weight of the corresponding subsample), DW SAV ss represents the dry weight of vegetation found in the subsample and DW (SAV+HE) ss represents the dry weight of the entire subsample (including SAV and attached herring eggs), and DW RS represents the dry weight SAV of the remaining part of the sample (exclusive subsample). The factor 2 was used to calculate the SAV biomass found on 0.5 m width beach transect to 1 m beach width.

Estimating the Number of Eggs in the Washed SAV Litter

The number of eggs found on a beach width of 1 m was estimated using Eq. (2). Mean egg number on 1 m beach stretch was then multiplied with the length of the investigated beach section (571 m) to extrapolate the number of eggs washed ashore in the investigated beach section.

Estimating the Impact of Storm Events during the Spawning Season 2012

Wind data provided by the DWD were used to identify distinct storm events during the herring spawning season in 2012. Using field observations on herring egg concentrations and SAV biomass during these periods, we calculated the observed maximum egg loss (EL Obs ) and the minimum egg loss (EL SAV ) for each storm event using the Eqs. (4) and (5). The cumulative egg loss during the spawning season in 2012 was then correlated to the adult spawners by estimating the corresponding reproductive equivalent RE (number of adult spawners needed to produce the number of lost herring eggs). Assuming that a female spawner carries approximately 45,500 eggs (Anwand 1962; Kändler and Dutt 1958), RE was calculated using the following equation:
$$ RE=\left(\left(\frac{EL_{2012}}{45500}\right)\ast 2\right) $$
(7)

where EL 2012 represents the cumulative egg loss during the spawning season in 2012 and the factor 2 was used to extrapolate the total number of spawners assuming a balanced gender ratio. The calculation was done twice: first using the observed (maximum) egg loss (EL 2012  = sum of all EL Obs ) and in a second run using the minimum egg loss (EL 2012  = sum of all EL SAV ).

Statistical Analyses

All statistical analyses were performed with STATISTICA 12 (Statsoft). Statistical significance was tested by using a one-way analysis of variance (ANOVA) with a significance level of p < 0.05. Data sets were tested for homoscedasticity by Levene’s test and were logarithmically transformed log(x+1) if necessary to meet the requirements of ANOVA.

Results

Herring Egg Concentrations from Different Spawning Zones

With an area of 1.84 km2, the largest spawning zone is located in 2–3 m depth (C), followed by the shallow zone in 1 m depth (A = 1.77 km2) and the spawning zone in 1.5 m (B = 0.55 km2). For determining the importance of different spawning zones, we estimated the mean egg number per spawning zone for the three different depth zones (Fig. 2), based on egg number and SAV biomass data per square meter (Online Resource 4). Prior to the storm event, spawning zone A was the most important spawning zone with the highest herring egg concentration in comparison to the other two spawning zones B and C. Although the spawning zone C includes the largest surface area on the spawning ground, the highest egg numbers were found in the shallow spawning zone A (Fig. 2). However, due to the high patchiness of egg distribution, the observed differences in egg concentrations were not statistically significant (ANOVA, F(2,15) = 3.13, p = 0.073).
Fig. 2

Herring egg distribution from three different spawning zones (1 m depth (A), 1.5 m depth (B), 2 m depth (C)) prior to the storm, based on calculated mean egg numbers per spawning zone (n = 6). Standard deviations are indicated by error bars

Analysis of Beach Litter Composition

SAV composition from the different transects in the distinct spawning zones was compared with the SAV composition found as litter on the lee side of the beach section immediately after the storm event (Table 1). It is supposed that most of the SAV litter originated from spawning zone A because the SAV biomass found on the beach consisted mainly of pondweed (Potamogetonaceae, Ruppiaceae) (93%) with a small amount of Ceratophyllum sp. (2%), resembling the composition of aquatic plants from that depth zone. Additionally, a small quantity of Zostera spp. was found in the beach litter, probably originating from the deeper spawning zones. We estimated that 63.6 million herring eggs were washed ashore along the investigated beach section with a length of 571 m.
Table 1

SAV composition in percent on transects A (1 m depth), B (1.5 m depth), and C (2 m depth) (prior to storm, 27th of March 2012) and composition of beach litter, found after a storm event (5th of April 2012) on a beach section located at the lee site of one important spawning ground in Greifswald Bay

Genus

Litter

A

B

C

Zostera spp.

5

0

27

100

Stuckenia sp./Ruppia sp.

93

85

73

0

Ceratophyllum sp.

2

10

0

0

Filamentous brown algae

0

5

0

0

Storm-Induced Egg Loss in the Pre-impact/Post-impact Experiment

Results revealed a reduction in SAV biomass and egg numbers after the storm event (Table 2), but the observed differences between the SAV biomass g DW m−2 (ANOVA, F 1,10 = 1.51, p = 0.247) and the mean egg number per gram DW SAV (ANOVA, F 1,10 = 4.03, p = 0.072) were not statistically significant. However, mean egg numbers per square meter significantly differed prior to and after the storm (ANOVA, F 1,10 = 5.55, p = 0.040). We observed a total egg reduction of 43,692 herring eggs, which corresponds to an egg loss of 94% (max. egg loss).
Table 2

Pre-impact/post-impact experiment mean and median values of SAV biomass gram DW per square meter (±standard deviation (SD)), egg number per gram DW SAV (±SD), and egg number per square meter (±SD) are given before/after a multiple day storm event in spring 2012 (28th of March–4th of April 2012)

  

Prior to storm

After storm

g DW SAV m−2

Mean (±SD)

Median

101.9 (±40.7)

106.3

81.8 (±31.6)

71.0

eggs g−1 DW SAV

Mean (±SD)

Median

393 (±408)

350

44 (±77)

0

eggs m−2

Mean (±SD)

Median

46,533 (±45,197)

41,394

2841* (±4652)

0

Asterisks indicate significant differences in egg numbers per square meter (ANOVA, F 1,10 = 5.55, p = 0.040)

The minimum egg loss (EL SAV ) was 29%, corresponding to a reduction of 13,724 eggs in total. Considering these two egg losses for the entire spawning area A, the estimated egg loss during the first storm event ranged from 17.2 (min.) to 55.7 billion eggs (max.). These calculated egg loss numbers for the shallow spawning zone A are equivalent to a reproductive potential loss of 756,000 adult herring (min.) and 2,448,000 adult herring (max.), respectively.

Storm-Induced Egg Loss During the Spawning Season 2012

Based on daily maximum wind speeds (m s−1) with respective wind directions affecting the spawning ground, we identified four storm events during the spawning season 2012 (Fig. 3). We examined the egg loss per square meter for each storm event to evaluate the cumulative storm impact during the entire spawning season, based on data from natural spawning bed transects. The estimated egg numbers for each storm event and the resulting total egg loss for season 2012 are presented in Table 3. The strongest storm event occurred between 28th March and 4th April 2012, when the egg loss experiment was conducted. This first storm included circulating wind directions, starting with north-westerly winds (290°) with maximum speeds of 16.6 m s−1 on March 28th. Wind speed increased up to 18.3 m s−1 on 1st April. During this initial storm, wind directions shifted from north-westerly to north-easterly winds (70°) of 13.8 m s−1 on April 4th. The dominant wind directions and wind speeds for Greifswald Bay between March 15th and May 25th, 2012 are given in Fig. 4 (additional data are given in Online Resource 5). We combined these data with our data on SAV biomass (g DW m−2) at the shallow transect and defined storm action by a minimum wind speed of 12 m s−1. We found a slight reduction in SAV biomass after the first storm event (end of March) in this shallow area. There were subsequent SAV biomass losses after the following three consecutive storm events. The minimum and maximum egg loss numbers for the first storm event are the values calculated from the experiment units. The estimated egg loss for the entire spawning season 2012 was 53.2 billion (min.) and 260.5 billion eggs (max.).
Fig. 3

Mean SAV biomass g DW m−2 (bars) with standard deviation along the spawning season 2012. The gray line represents the daily maximum wind speed (m s−1), and prevailing wind directions during four storm events are given in abbreviation. The first storm event end of March was characterized by changing wind regimes (north-westerly to north-easterly)

Table 3

Estimated egg loss of the each storm event in the season 2012

 

Egg loss in billions

Storm event

Min.

Max.

28th March–04th April

17.2

55.7

11th April–19th April

32.7

94.7

27th April–03rd May

3.3

110.1

11th May–14th May

0

0

Estimated egg loss per spawning zone A (1 m depth) in 2012

53.2

260.5

The storm-induced observed egg loss numbers are indicated by maximum egg loss. Following the assumption that any loss of SAV directly results in a loss of adherent herring eggs, this calculated egg loss numbers are indicated as the minimum egg loss on the shallow spawning zone A (1 m depth). Minimum and maximum values for the first storm event are based on results from the experiment, and the other egg loss values are based on transect egg concentration data, taken along the entire spawning season 2012

Fig. 4

Dominant wind directions and wind speeds for Greifswald Bay between March 15th and May 25th, 2012, based on hourly measurements provided by Germany’s National Meteorological Service (DWD). Wind speeds are given in distinct categories (green indicates hours with a measured wind speed of ≤5 m s−1; yellow indicates hours with a wind speed between 5 and 10 m s−1, orange indicates a wind speed between 10 and 15 m s−1 and red a wind speed of >15 m s−1) (Color figure online)

Discussion

This study revealed a substantial impact of storm-induced wave action on herring egg mortality. During a multiple day storm event, we observed an egg loss which ranged between 29 and 94%. According to the observation that adherent herring egg abundances are strongly related to SAV, rather than to hard substrates (Kanstinger et al. 2016), heavy storms can damage and uproot aquatic vegetation and cause considerable loss in plant canopies (Cruz-Palacios and van Tussenbroek 2005; Mataraza et al. 1999) and consequently leading to the loss of attached fish eggs. We found a total of 63.6 million eggs on vegetation washed ashore along a 571 m long stretch of the leeward shore of the spawning ground.

Considering the similar composition of plant taxa in the SAV litter on the beach and the macrophyte community found in the shallower spawning areas (mainly zone A), we conclude that the majority of the egg loss occurred in the spawning beds in the immediate proximity to the shore line. Furthermore, the hydrodynamic effects of waves on SAV are probably more pronounced in the shallower areas; however, we cannot exclude that deeper areas were also affected as indicated by a minor proportion of seagrass in the SAV litter washed ashore.

Prevailing wind directions during the storm implied that the vegetation washed ashore probably originated from the spawning ground Gahlkower Haken. We did not find any loose eggs detached from the vegetation washed up on the beach. Concerning the quantification of egg numbers found on the beach site, we assume that estimated egg numbers found on SAV litter most likely are an underestimation of the entire egg loss. This is due to the observation of high amounts of vegetation retrieved from the beach caused by wave action during the storm before sampling could be performed (Online Resource 6). Hourston and Rosenthal (1967) described that after a series of storms, high numbers of detached eggs were found in the intertidal zone during low tide. Survival analyses in the laboratory indicated that eggs attached to vegetation showed an overall higher hatching rate than the un-adhered eggs. Compared to the Pacific Ocean, lunar tides are marginal in the Baltic Sea. Therefore, air exposure time is the influencing factor for egg survival, causing hypoxia, and desiccation and air exposure of washed up fish eggs depend on irregular wave forcing (Jones 1972). According to our observations (unpublished), most of the eggs washed back into the water did not survive but accumulated at the sea bottom in sheltered waters creating thick layers consisting of decaying SAV and eggs.

Our estimated numbers of herring eggs per square meter detached and exported from the spawning ground by storm action was equivalent to 94% of the initial egg number (Table 2). Since this egg loss might at least be partially influenced by other factors such as hatching or predation, we consider the 94% to be the uppermost limit of storm-induced egg loss. Based on SAV reduction in experimental units, we estimated a mean minimum egg loss of 29% (based on pairwise comparisons). Since this value does not include those eggs that are detached from the plants but only eggs that are still attached and lost through the removal of plants, we consider the 29% to be the lowermost limit of storm-induced egg loss. This storm effect and loss of eggs which occurred at the end of March most likely is an underestimation of the de facto storm-induced loss, yet a 29% minimum egg loss represents a considerable extent of total spawning capacity. However, extrapolating these effects to the total spawning area of Greifswald Bay or the total population remains difficult since storm effects must be considered to be strongly site- and habitat-specific.

Considering both the minimum (EL SAV ) and maximum value (EL Obs ), the estimated egg loss for the entire shallow spawning zone in the investigation area during the storm event at the end of March ranged between 17.2 and 55.7 billion herring eggs. The average number of eggs per female of western Baltic herring is 45,500 (Anwand 1962; Kändler and Dutt 1958). Our projection revealed that the storm-induced loss of eggs found during our experiment would result in a minimum equivalent of the reproductive loss of approximately 756,000 adult individuals (129.3 tons). With a magnitude of approximately 0.15%, the proportion of this reproductive capacity loss in relation to the total number of reproductive 3+ group herring in the Western Baltic Sea (506 million individuals, 74.0 kt) estimated for the particular year 2012 (ICES 2015) seems marginal but represents only the absolute storm-induced egg loss on a small single spawning bed. For Pacific herring in British Columbia, Canada, Hay and Miller (1982) indicated a 576 t loss of reproductive capacity caused by storms. Although our findings in the Baltic Sea indicate only about a fourth of this amount, such loss in reproductive capacity is a remarkable number considering that our results represent only one storm event out of many storm events in only one out of multiple spawning areas. Regarding the entire spawning season 2012 with four consecutive storm events, we calculated a total egg loss of 53.2 and 260.5 billion eggs in the shallow spawning zone. These additive results would have led to a minimum reproductive capacity loss of 2.3 million individuals (393 t) and a maximum capacity loss of 11.4 million individuals (1.9 kt) revealing a total loss of 0.5–2.3% of total matured 3+ group herring individuals.

Since vegetation cover and seasonal succession are generally dynamic due to varying temperatures and light regimes (Blümel et al. 2002; Dennison 1987; Munkes 2005a), spatial extension of spawning zones in 2012 might have slightly differed from conditions during the herring spawning season 2009 where aerial images of SAV distribution were obtained (Kanstinger et al. 2016).

However, the overall impact of local storm events on the reproductive capacity of inshore spawning herring in the Baltic Sea is most probably underestimated by our findings, but regarding cumulative effects of multiple storm events in different spawning areas and the overall multiple stressors, acting on the local scale of spawning grounds, it can be assumed that all these stressors unquestionably have a negative impact on population level. Furthermore, hydrodynamic forces might also affect the condition and development of herring eggs and should be considered in further research.

This is supported by findings of the annual larval herring monitoring in Greifswald Bay which samples larval abundance during the reproduction period on a weekly basis to provide an annual recruitment index for stock assessment of the Western Baltic spring spawning herring (Oeberst et al. 2009; Polte et al. 2014). Greifswald Bay is considered a major spawning ground for the Western Baltic population because the larval herring production in the basin regularly correlates strongly with the number of 1-group juveniles in the overall Western Baltic Sea (Oeberst et al. 2009; Polte et al. 2014). In 2012, the total number of larval herring produced in the system was one of the lowest in two decades of larvae monitoring (ICES 2013). Furthermore, the number of 1-group juveniles 1 year later was correspondingly low (ICES 2014). Although there is no solid evidence for a causal relation between this cascade of recruitment failure and local storm events, there is a strong potential that high storm-induced egg mortality has largely contributed to the multiple stressors and interactions leading to minor recruitment in 2012. Although the comparably low larval herring numbers found during the season 2012 cannot be directly proven to be linked to storm-induced egg mortality, it is unlikely that the low numbers of newly hatched larvae were due to reduced spawning activity for two reasons: (i) The initial egg numbers found on the spawning bed were similar to other years (D. Moll unpublished), and (ii) herring fishery on the spawning grounds took its quota in a relatively short period compared to the previous year, indicating massive herring runs.

Our results reveal a substantial storm impact on herring egg mortality, noting that we only investigated one out of multiple spawning grounds. The cumulative effects of multiple storms in different spawning areas should be considered in further storm impact investigations. Wind events in general can also have positive effects on population dynamics, e.g., providing a well-oxygenated water body (Munkes 2005b; Schoknecht 1973) and improving herring egg development or better feeding conditions for herring larvae (e.g. Ojaveer et al. 1998). However, considering scientific forecasts of increasing storm intensities and frequency (Coumou and Rahmstorf 2012; Woth et al. 2006), this study has highlighted the fact that different anthropogenic stressors (e.g. eutrophication and climate change) can act as hazardous stressors for herring reproduction success.

Conclusions

The results of the study indicate that depth distribution of vegetated spawning zones and related egg concentrations affect the magnitude of herring egg mortality caused by storm events during the reproduction period. Anthropogenic impacts in the Baltic Sea have already resulted in a limited depth distribution of submerged aquatic vegetation leading to an increased exposure of herring spawning beds to storm-induced wave action. We conclude that regional storm events are crucial stressors on the reproduction of inshore spawning herring and potentially other fish species. In addition to other local stressors, such as coastal modification and predation by the local estuarine fish community (Kotterba et al. 2014), synergistic impacts of eutrophication and increasing storm frequencies (Coumou and Rahmstorf 2012; Woth et al. 2006) might pose a threat to herring egg survival in inner coastal waters. Regional coastal zone management should consider these types of ecological cascades and implement appropriate strategies to maintain crucial habitats for early life stages of fish populations since local stressors negatively affect the population dynamics at higher spatial scales.

Notes

Acknowledgements

We would like to thank the many student helpers and our colleagues from the Thünen Institute of Baltic Sea Fisheries who contributed to this study, especially Rainer Oeberst and Dr. Marta Moyano from the University of Hamburg who provided constructive comments and suggestions on the Figures. We would like to thank the editors and three anonymous reviewers for their helpful comments. Thanks are extended to the German National Meteorological Service (DWD) for providing the wind data. The research leading to these results received funding from BONUS INSPIRE (D.M.) and BONUS BIO-C3 (P.K.), the joint Baltic Sea research and development program (Art 185), funded jointly by the European Union and the Federal Ministry of Education and Research of Germany (BMBF 03F0681; 03F0682). L.v.N. received funds from the German Federal Environmental Foundation (DBU), and P.P. was funded by the EU Data Collection Framework (DCF).

Supplementary material

12237_2017_259_MOESM1_ESM.pdf (675 kb)
ESM 1 (PDF 675 kb)

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Copyright information

© Coastal and Estuarine Research Federation 2017

Authors and Affiliations

  1. 1.Thünen-Institute of Baltic Sea FisheriesRostockGermany
  2. 2.Institute of Hydrobiology and Fisheries ScienceUniversity of HamburgHamburgGermany

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