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Map of Strelasund and the Greifswalder Bodden with the 35 sampling stations and the strata used in the analysis (I, 69 km 2 , five stations; II, 100 km 2 , seven stations; III, 95 km 2 , seven stations; IV, 138 km 2 , seven stations; V, 105 km 2 , nine stations). Inset shows the area
Source publication
Oeberst, R., Klenz, B., Gröhsler, T., Dickey-Collas, M., Nash, R. D. M., and Zimmermann, C. 2009. When is year-class strength
determined in western Baltic herring? – ICES Journal of Marine Science, 66: 1667–1672.Weekly surveys of larvae in the Strelasund and the Greifswalder Bodden were used to investigate when year-class strength is
determined in...
Contexts in source publication
Context 1
... is a good indicator of the spawning-stock biomass (SSB), whereas the abundance of large larvae is strongly correlated with recruitment at age 1 (ICES, 2007). Our principal objective was to determine whether an index of early larval abundance is correlated with independent estimates of recruitment in WBSS herring, and thereby to establish when the processes that determine year-class strength operate. Surveys of larval abundance in the Strelasund and the Greifswalder Bodden (Figure 1) have been conducted annually since 1992. During the spawning season, up to 14 surveys have been conducted weekly, using stepwise oblique hauls with a 60-cm Bongo net (mesh size 0.335 mm; M ̈ller and Klenz, 1994). All samples were taken in daylight and preserved in 4% buffered formaldehyde-seawater solution immediately after collection. In principle, each weekly survey covered 35 stations, but occasionally not all stations could be sampled. The area was sub- divided into five strata for processing catch rates (Figure 1). The time taken to complete all stations within a cruise varied between 2 and 5 d because of bad weather or technical problems (cf. Table 1). The surveys initially started in week 14 but, from 1998 on, the first survey was delayed until week 17 because analyses had shown that the first weeks yielded low catches of the larger larvae that were considered more meaningful for estimating year- class strength. Herring larvae were identified, counted, and measured (total length to 1 mm below) in the laboratory. No corrections were made for shrinkage caused by fixation. For large samples ( . 1000 larvae), three subsamples of 200 larvae were measured and the remaining larvae were counted. The size distributions of the subsamples were raised to the total number caught. For each station i of week j and stratum h , the number of larvae by length group k per m 2 ( C i , h , j , k ) was estimated, based on the volume of filtered water (measured by flowmeter) and the maximum sampled water depth of the tow (M ̈ller and Klenz, 1994). The mean daily growth of the larvae (DG, mm d 2 1 ) was estimated from a linear regression on surface temperature ( T in 8 C; Oeberst et al ., 2009) as ...
Context 2
... is a good indicator of the spawning-stock biomass (SSB), whereas the abundance of large larvae is strongly correlated with recruitment at age 1 (ICES, 2007). Our principal objective was to determine whether an index of early larval abundance is correlated with independent estimates of recruitment in WBSS herring, and thereby to establish when the processes that determine year-class strength operate. Surveys of larval abundance in the Strelasund and the Greifswalder Bodden (Figure 1) have been conducted annually since 1992. During the spawning season, up to 14 surveys have been conducted weekly, using stepwise oblique hauls with a 60-cm Bongo net (mesh size 0.335 mm; M ̈ller and Klenz, 1994). All samples were taken in daylight and preserved in 4% buffered formaldehyde-seawater solution immediately after collection. In principle, each weekly survey covered 35 stations, but occasionally not all stations could be sampled. The area was sub- divided into five strata for processing catch rates (Figure 1). The time taken to complete all stations within a cruise varied between 2 and 5 d because of bad weather or technical problems (cf. Table 1). The surveys initially started in week 14 but, from 1998 on, the first survey was delayed until week 17 because analyses had shown that the first weeks yielded low catches of the larger larvae that were considered more meaningful for estimating year- class strength. Herring larvae were identified, counted, and measured (total length to 1 mm below) in the laboratory. No corrections were made for shrinkage caused by fixation. For large samples ( . 1000 larvae), three subsamples of 200 larvae were measured and the remaining larvae were counted. The size distributions of the subsamples were raised to the total number caught. For each station i of week j and stratum h , the number of larvae by length group k per m 2 ( C i , h , j , k ) was estimated, based on the volume of filtered water (measured by flowmeter) and the maximum sampled water depth of the tow (M ̈ller and Klenz, 1994). The mean daily growth of the larvae (DG, mm d 2 1 ) was estimated from a linear regression on surface temperature ( T in 8 C; Oeberst et al ., 2009) as ...
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Citations
... The total abundance of age 2 individuals were taken from the stock assessment (Rolland et al. 2022) and were lagged 2 years (hereinafter referred to as recruitment) so that recruitment could be related to the physical (i.e., environmental) and biological conditions in the year the cohort was spawned. When referring to year throughout the manuscript, we are referring to the year the cohort was spawned rather than the year recruits turned age 2. Research suggests that recruitment levels are typically determined in the first few months of life, with the majority of mortality occurring in the egg and early-mid larval stages (Houde 1987;Saetre et al. 2002a;Nash and Dickey-Collas 2005;Oeberst et al. 2009;Moyano et al. 2022). Therefore, lagging the abundance of age 2 individuals by 2 years is appropriate and required to evaluate the factors influencing recruitment. ...
Despite the importance of recruitment for population dynamics and assessing stock status, limited information exists on the relative influence of various ecosystem and demographic factors on the recruitment dynamics of marine fishes. We develop a statistical framework to identify the ecosystem and demographic factors influencing the recruitment and of marine fishes and facilitate improved predictions of recruitment. We demonstrate the approach by examining the relative influence of ecosystem and demographic factors on the recruitment of southern Gulf of St. Lawrence spring and fall spawning Atlantic Herring (Clupea harengus) stocks, highlighting the benefit of considering multiple factors to better understand recruitment trends. We found different combinations of biological and physical ecosystem factors along with demographic factors had a significant influence on the recruitment and recruitment rate of spring and fall spawning herring. The study emphasizes the value of considering ecosystem characteristics when examining recruitment, provides a framework for researchers to investigate and model recruitment of other fish populations, and supports the continued development and implementation of ecosystem based fisheries management approaches for species such as Atlantic Herring.
... Recruitment dynamics play an important role in fisheries management, and previous studies have hypothesized that in marine fishes, recruitment patterns are largely set in the larval stage (Hjort 1926, Bannister et al. 1974, Oeberst et al. 2009). Many challenges constrain efforts to determine biological and physical drivers that predict cohort strength from data collected at the earliest life stages; yet interactions between biological and physical drivers demonstrate the importance of evaluating survival across multiple early-life stages (Ottersen & Loeng 2000, Ottersen et al. 2014. ...
Juvenile Atlantic cod Gadus morhua in coastal Newfoundland, Canada, settle in nearshore habitats in 3-6 pulsed events each year, from mid-summer to early winter, creating a broad size range of age-0 fish within each year-class, with potentially different survival trajectories entering their first winter. We hypothesized that early-arriving pulses, high autumn body condition, and low winter severity would contribute to high first-winter survival of age-0 cod. To test these hypotheses, we examined juvenile cod catch data, archived samples, and winter temperature records to determine the impact of winter duration, body condition, and settlement time on winter survival from 2001 to 2019. Settlement pulse, pre-winter condition, and winter duration had an interactive effect on survival. Fish condition did not vary across pulses within a given year-class, but we observed improved winter survival during long winters when body condition was high, with highest survival among the larger, early-arriving pulses of fish. Late-arriving pulses of fish were small-sized before winter onset, but these pulses unexpectedly survived better than pulses settling earlier in the season during short winters when body condition was high. However, all settlement pulses had a survival advantage when settlement occurred earlier on average (associated with warmer autumns), suggesting that increased growth and size within a pulse can also contribute to overwintering success. Our findings challenge some assumptions of overwintering ecology (e.g. ‘bigger is better’, low temperature stress) but illustrate the importance of cohort effects for overwintering survival in a sub-arctic marine ecosystem in a changing climate.
... Furthermore, Houde [57] noted that small changes in mean vital rates in the later stages (50-70 days old) may have greater implications for recruitment than the loss of eggs and early larvae. By contrast, the early regulation of year-class strength has been proven for North Sea autumn-spawning herring [22,58] as well as for Baltic herring larvae at as early an age as 20-40 days old (20 mm length) [23]. ...
... Furthermore, Houde [57] noted that small changes in mean vital rates in the later stages (50-70 days old) may have greater implications for recruitment than the loss of eggs and early larvae. By contrast, the early regulation of year-class strength has been proven for North Sea autumn-spawning herring [22,58] as well as for Baltic herring larvae at as early an age as 20-40 days old (20 mm length) [23]. Our results suggest that year-class strength appears to be resolved some time after the two first months of life (i.e., the maximum age of the larvae in this study) and is most likely to be resolved in juvenile stages. ...
Abundance indices are essential data for the application of stock assessment models to obtain fish abundance estimates. Abundance indices have usually been derived from fishery-dependent data, yet the increase in fisheries-independent surveys is now offering new opportunities for these calculations. In this study, we explored the usefulness of ichthyoplankton indices derived from scientific surveys in estimating spawning biomass. In addition, we also investigated whether the strength of the year–class of the commercial cohort of Atlantic hake, as a determinant, could be defined at an early life stage. We used samples collected during the triennial mackerel and horse mackerel egg surveys (MEGS), which cover the hake spawning area in the Bay of Biscay. The biomass indices were determined as the abundance of eggs in the early development stage (stage 1) when transformed into egg production (EP) from 1995 to 2019 in the months of March and April—which is considered a period of high spawning activity for hake in this area. Additionally, we built a metric for larval abundance and converted larval length into age. This was in addition to constructing a pre-recruit year-class index (YCI) while using the EVHOE bottom trawl abundance database for hake for the period of 1997 to 2016. The results of regression analysis of egg production and spawning stock biomass indicate that both parameters are significantly correlated (r = 0.76). By connecting the abundance of eggs and larvae in the adjoining stages, we are able to identify two periods of high mortality associated with the transition from “yolk-sac-first” to “feeding larvae” and “late larvae-YCI10”, but we were unable to discover when the strength of the recruitment year–class is determined. As such, it appears that for the northern stock of hake, recruitment is established in the late juvenile stages.
... Its commercial importance motivated the research on recruitment mechanisms, leading to seminal recruitment hypotheses, such as the critical period (Hjort 1914) and member-vagrant (Iles and Sinclair 1982), as well as the establishment of the International Council for the Exploration of the Sea (ICES) at the beginning of the twentieth century (Sinclair 2009). Current knowledge supports that herring recruitment is set within the first month(s) of life in North East Atlantic populations, such as those in the Norwegian, North and Baltic Seas (Saetre 2002;Nash and Dickey-Collas 2005;Oeberst et al. 2009). Bottomup factors, such as changes in temperature and shifts in the abundance and type of prey, have been proposed as potential drivers of recruitment variability in these populations (Cardinale et al. 2009). ...
... Previous studies suggest that recruitment success in WBSS herring is established by the time larvae reach a total length of 20 mm (Oeberst et al. 2009). Based on this finding, a recruitment index (the N20) was created based on the number of larvae reaching 20 mm by the end of the spawning season (i.e., June) during the Rügen Herring Larval Survey (RHLS) in Greifswald Bay. ...
... Based on this finding, a recruitment index (the N20) was created based on the number of larvae reaching 20 mm by the end of the spawning season (i.e., June) during the Rügen Herring Larval Survey (RHLS) in Greifswald Bay. This N20 index seems to reflect well the recruitment dynamics of the whole WBSS stock and, thus, it has been used within the calculations of the recruitment index in the WBSS assessment (Oeberst et al. 2009;Polte et al. 2014;ICES 2021b). A second recruitment index, the GERAS age 1 index, is estimated based on the number of 1-yrold juveniles observed during the German Autumn Acoustic survey (GERAS). ...
Understanding the drivers behind fluctuations in fish populations remains a key objective in fishery science. Our predictive capacity to explain these fluctuations is still relatively low, due to the amalgam of interacting bottom-up and top-down factors, which vary across time and space among and within populations. Gaining a mechanistic understanding of these recruitment drivers requires a holistic approach, combining field, experimental and modelling efforts. Here, we use the Western Baltic Spring-Spawning (WBSS) herring ( Clupea harengus ) to exemplify the power of this holistic approach and the high complexity of the recruitment drivers (and their interactions). Since the early 2000s, low recruitment levels have promoted intense research on this stock. Our literature synthesis suggests that the major drivers are habitat compression of the spawning beds (due to eutrophication and coastal modification mainly) and warming, which indirectly leads to changes in spawning phenology, prey abundance and predation pressure. Other factors include increased intensity of extreme climate events and new predators in the system. Four main knowledge gaps were identified related to life-cycle migration and habitat use, population structure and demographics, life-stage specific impact of multi-stressors, and predator–prey interactions. Specific research topics within these areas are proposed, as well as the priority to support a sustainable management of the stock. Given that the Baltic Sea is severely impacted by warming, eutrophication and altered precipitation, WBSS herring could be a harbinger of potential effects of changing environmental drivers to the recruitment of small pelagic fishes in other coastal areas in the world.
Graphical abstract
... Greifswald Bay is an inshore coastal lagoon in the southern Baltic Sea. The bay is considered as major spawning and nursery ground for spring spawning herring, since several herring cohorts immigrate for spawning and larval herring production in the system correlates with recruitment indices of the entire stock (Oeberst et al., 2009;Polte et al., 2014). The Warnow River drains into the southern Baltic Sea about 100 km westwards of Greifswald Bay. ...
... Across herring populations, the year-class strength is assumed to be determined during the early life stages and mostly related to the larval stage (Fox and Aldridge., 2002;Nash and Dickey-Collas, 2005;Oeberst et al., 2009). According to the current state of knowledge this is very plausible due to the critical period (Hjort, 1914) and associated bottlenecks, such as prey match/mis-match (Cushing, 1969;Durant et al., 2007), affecting survival and growth, often structuring recruitment (e.g. ...
Fish species classified as demersal substrate spawners attach their eggs on macrophytes or hard substratum. As a
result of consecutive events of egg deposition and successive waves of spawning adults, the formation of multiple
egg layers on spawning substrate was already observed for demersal spawning fish species, such as the Atlantic
herring (Clupea harengus, Linnaeus 1758). However, empirical information on the effects of egg concentration on
herring egg survival are scarce. Hypothesizing that high egg concentration has a negative effect on herring egg
survival, we experimentally investigated egg mortalities in artificially spawned egg clusters of 1 – 10 vertical
layers. Moreover, potential effects of egg packing density (loose and dense) in one single layer set-up were
investigated. We found decreasing herring egg survival in clusters of increasing egg layers. Although egg mor-
tality was lowest in surface layers, a layerwise analysis did not reveal a stepwise increase of egg mortality from
outermost to innermost eggs. Furthermore, egg mortality in surface layers significantly increased with increasing
egg layer number underneath, indicating that the extent of layering is affecting egg mortality also in surface
layers. This implies a reproductive disadvantage not only for eggs being spawned in the beginning of consecutive
spawning events but also for latest spawned eggs. However, different egg packing densities in single layer spawn
did neither influence the fertilization success nor mortality of herring eggs. Increased thickness of egg layers may
result from the ongoing reduction and fragmentation of coastal vegetation due to eutrophication and coastal
modifications. Our results indicate that such multiple egg layer deposition has the potential to negatively impact
hatching success in demersal spawning fish species, such as the herring.
... Understanding the distribution patterns of different life stages and exploring the abiotic and biotic factors influencing the distribution patterns are crucial for assessing fish recruitment and stock restoration (Costa et al., 2002;Santos et al., 2017). Achieving the sustainability of fishery resources depends highly on the abundance and survival of fish eggs and larvae (Oeberst et al., 2009;Llopiz et al., 2014). ...
... Young-of-the-year (YOY) Atlantic herring (Clupea harengus) and European sprat (Sprattus sprattus) are found in both the Skagerrak and Kattegat with the two species constituting the 2 nd and 6 th most abundant members of the ichthyoplankton between 1992 and 2010 . YOY fish are of ecological importance as they determine year class strength and following recruitment success of populations (Oeberst et al., 2009;Eriksen et al., 2011). A major cause for the recruitment variation is the occurring mortality of YOY fish during the early stages of development (Hjort, 1914;Houde, 2008). ...
... Thus, the change in variability of AAIs is most likely not ecologically driven. All in all, the estimates AAIs using 0-group fish could represent valid estimates for recruitment as most mortality happens in early life stages (Oeberst et al., 2009;Moyano et al., 2020). ...
Coastal areas are important habitats for early life stages of many fish species. These habitats are used as nursery grounds and can provide a significant contribution to the recruitment of a fish population. In 1919, standardized sampling with a beach seine along the Norwegian Skagerrak coastline was established mainly to target 0-group fish. Here, we focus on Atlantic herring and European sprat to explore whether inter-annual variability in the abundance of these species is indicative of variability in recruitment. We investigated if the abundance of 0-group herring and sprat are affected by environmental factors. Further, the beach seine abundance indices were compared with recruitment estimates of neighboring stocks. There was a clear correlation between herring and sprat abundance in the beach seine samples. While sprat abundance was mainly affected by environmental factors such as temperature and current drift, herring abundance was positively affected by the recruitment of the neighboring stock of western Baltic spring spawners. One plausible explanation could be that sprat recruit to a more local component, while herring of the neighboring stock utilize the Skagerrak coastline as nursery grounds. This study demonstrates the importance of long time series and can provide new insight into the dynamics and structure of multiple fish species.
... Year-class strength of western Baltic C. harengus is determined early in life (Oeberst et al. 2009). Factors affecting survival of larvae >20 mm in length and juveniles are similar amongst years, suggesting that inter-annual variability in mortality of the late-stage larvae and juveniles is relatively small. ...
The patterns of reproduction and ontogeny in the taxonomically rich estuary- associated fishes from around the world, including all the major groups from elasmobranchs to bony fishes, are as diverse as documented for fishes living in other ecosystems. Further, the diversity in the life histories of bony fishes, the dominant group in estuaries, is complex with egg, larval, juvenile and adult stages having different ecologies. Moreover, some stages that use estuaries also use the adjacent, downstream ocean and upstream
freshwaters for parts of their life history. Given these complexities in patterns and modes of reproduction, it is not surprising that recruitment processes of estuary- associated fishes are also diverse. In Introduction, we listed six broad topic areas that framed the scope of the chapter. We summarise major conclusions in the six areas.
... It has serious consequences for sustaining the capture fisheries sector. In the current paradigm of fisheries management, recruitment variability relative to the mean population abundanceN is thought to be lower in stocks with more spawning components (Myers 2001, Oeberst et al. 2009). In stark contrast, the observed proportionality between the mean recruitmentR and the sample standard deviation Σ R implies that the relative fluctuations Σ R /N do not decay with higher mean abundances. ...
This paper studies the scaling properties of recruitment fluctuations in randomly varying environments, for abundant marine species with extreme reproductive behavior. Fisheries stock-recruitment data from the North Atlantic display fluctuation scaling, a proportionality between the standard deviation and the average recruitment among stocks. The proportionality covers over five orders of magnitude in the range studied. A linear-scaling behavior can be a sign of a universal distribution of the normalized data across stocks. In light of this conjecture, it is demonstrated that the L\'evy-stable model offers a better effective description of the recruitment distribution than the log-normal model. Care is devoted to the problem of random sums of random variables. Recruitment is calculated by summing random offspring numbers with infinite variance, where the number of summands (i.e. spawning population size) is also a random process with infinite variance.
... It has serious consequences for sustaining the capture fisheries sector. In the current paradigm of fisheries management, recruitment variability relative to the mean population abundanceN is thought to be lower in stocks with more spawning components (Myers 2001, Oeberst et al. 2009). In stark contrast, the observed proportionality between the mean recruitmentR and the sample standard deviation Σ R implies that the relative fluctuations Σ R /N do not decay with higher mean abundances. ...
Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item interaction graphs. In order to reduce the influence of data sparsity, contrastive learning is adopted in graph collaborative filtering for enhancing the performance. However, these methods typically construct the contrastive pairs by random sampling, which neglect the neighboring relations among users (or items) and fail to fully exploit the potential of contrastive learning for recommendation. To tackle the above issue, we propose a novel contrastive learning approach, named Neighborhood-enriched Contrastive Learning, named NCL, which explicitly incorporates the potential neighbors into contrastive pairs. Specifically, we introduce the neighbors of a user (or an item) from graph structure and semantic space respectively. For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs. In implementation, the representations of users (or items) and neighbors correspond to the outputs of different GNN layers. Furthermore, to excavate the potential neighbor relation in semantic space, we assume that users with similar representations are within the semantic neighborhood, and incorporate these semantic neighbors into the prototype-contrastive objective. The proposed NCL can be optimized with EM algorithm and generalized to apply to graph collaborative filtering methods. Extensive experiments on five public datasets demonstrate the effectiveness of the proposed NCL, notably with 26% and 17% performance gain over a competitive graph collaborative filtering base model on the Yelp and Amazon-book datasets respectively. Our code is available at: https://github.com/RUCAIBox/NCL.
