Recruitment Forecasting Research Articles

Leveraging ecological indicators to improve short term forecasts of fish recruitment

AbstractForecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

Testing novel methods for short-term forecasting of European glass eel recruitment

Recruitment forecasting constitutes a major issue in population dynamics, especially in stock assessments. In many cases, high recruitment stochasticity thwarts the determination of a stock-recruitment (SR) relationship. When no SR relationship is available, recruitments can be projected from past trends using methods such as the geometric means of the past recruitments. This approach implicitly assumes stability in upcoming years and might not be appropriate in the case of regime shifts. Recruitment forecasting is particularly critical when predicting fishing opportunities for recruitment fisheries. The fishery of glass European eels (Anguilla anguilla) is an example of a recruitment fishery. The French glass eel fishery, which is currently guided by two models to forecast upcoming recruitments and derive fishing opportunities, has failed to achieve management targets. This study develops new models that forecast glass eel recruitments without assumptions about the SR relationship, and with flexible assumptions about future recruitment evolutions. Tests based on multiple criteria found the best performance in a random slope model, which provides flexibility to track recruitment dynamics while balancing the interests of fisheries, management and conservation. This model has wide potential for trend projections in natural resource management, economics and other fields.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies.

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

A framework for assessing the skill and value of operational recruitment forecasts

Abstract Forecasting variation in the recruitment to fish stocks is one of the most challenging and long-running problems in fisheries science and essentially remains unsolved today. Traditionally, recruitment forecasts are developed and evaluated based on explanatory and goodness-of-fit approaches that do not reflect their ability to predict beyond the data on which they were developed. Here, we propose a new generic framework that allows the skill and value of recruitment forecasts to be assessed in a manner that is relevant to their potential use in an operational setting. We assess forecast skill based on predictive power using a retrospective forecasting approach inspired by meteorology, and emphasize the importance of assessing these forecasts relative to a baseline. We quantify the value of these forecasts using an economic cost-loss decision model that is directly relevant to many forecast users. We demonstrate this framework using four stocks of lesser sandeel (Ammodytes marinus) in the North Sea, showing for the first time in an operationally realistic setting that skilful and valuable forecasts are feasible in two of these areas. This result shows the ability to produce valuable short-term recruitment forecasts, and highlights the need to revisit our approach to and understanding of recruitment forecasting.

Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model

Fishery managers often rely on forecasts of future population abundance to set allowable harvest quotas or exploitation rates. While there has been substantial research devoted to identifying environmental factors that can predict recruitment for individual populations, such correlations often degrade over time, thereby limiting their utility for management. Conversely, examining multiple populations at once to detect shared, spatially structured patterns can offer insights into their recruitment dynamics that are advantageous for forecasting. Here, we develop a population dynamics model for natural origin coho salmon (Oncorhynchus kisutch) stocks in Washington State that leverages spatial and temporal autocorrelation in marine survival to improve one-year-ahead forecasts of adult returns. Executed in a Bayesian hierarchical integrated modelling framework, our spatiotemporal approach incorporates multiple data types and shares information among stocks to estimate key biological parameters that are informative for forecasting. Retrospective evaluation of one-year-ahead forecast skill indicated that the spatiotemporal integrated population model (ST-IPM) outperformed existing forecasts of Washington State coho salmon returns by 25–38 % on average. Moreover, the ST-IPM estimates parameters that were previously non-identifiable for many stocks, and propagates uncertainty from multiple contributing data sources into model forecasts. Our results add to a growing body of work demonstrating the utility of spatiotemporal and integrated approaches for modelling population dynamics, and the framework developed here has broad applications to the assessment and management of coho salmon in Washington State and elsewhere throughout their range.

The use and performance of survey-based pre-recruit abundance indices for possible inclusion in stock assessments of coastal-dependent species

Abstract We reviewed the use of survey-based pre-recruit abundance indices in short-term recruitment forecasts for fish species relying on coastal habitats at the juvenile stage and that are assessed by ICES. We collated information from stock assessment reports and from a questionnaire filled out by the stock assessors. Among the 78 stocks with juvenile coastal dependence, 49 use short-term forecasts in stock assessment. Survey-based pre-recruit abundance indices were available for 35 of these stocks, but only 14 were used to forecast recruitment. The questionnaire indicated that the limited use of survey-based pre-recruit abundance indices was primarily due to sampling inefficiency, which may preclude reliable recruitment estimates. The sampling is inefficient because the juvenile coastal distribution is outside the geographical area covered by large-scale surveys or targeted coastal surveys are conducted on limited spatial and temporal scales. However, our analysis of the relationship between survey-based pre-recruit indices and assessment-generated recruitment indices revealed that survey-based pre-recruit abundance indices were sufficiently accurate to provide useful information for predicting future recruitment. We recommend expansion of the use of survey-based indices of pre-recruit abundance in stock assessment and recruitment forecasting, and consideration of how to include juveniles in ongoing and future surveys.

Environmentally driven forecasts of northern rock sole (Lepidopsetta polyxystra) recruitment in the eastern Bering Sea

AbstractNorthern rock sole recruitment in the eastern Bering Sea has been hypothesized to (a) depend on wind‐driven surface currents linking spawning and nursery areas, (b) be density‐dependent, and (c) be negatively impacted by cold bottom temperatures over a large nursery area during the first summer of life. A suite of models was developed to test these hypotheses. Data included 32 years of recruitment and spawning biomass estimates derived from a stock assessment model and wind and temperature indices customized to the environmental exposure of age‐0 northern rock sole in the eastern Bering Sea. The predictive ability of the models was evaluated, and the models were used to forecast recruitment to age‐4 for recent year classes which are poorly retained by the standard multi‐species bottom trawl survey gear. Models which included wind and temperature indices performed better than a naïve forecast based on the running mean. The best‐performing model was a categorical model with wind and temperature thresholds, which explained 49% of the variation in recruitment. Ricker models performed more poorly than models without a spawning biomass term, providing no evidence that recruitment is related to stock size. The models forecast higher recruitment for the most recent year classes (2015–2018) than for prior year classes with observed poor recruitment (2006–2013). These environment‐based recruitment forecasts may improve recruitment estimates for the most recent year classes and facilitate study of the effects of future climate change on northern rock sole population dynamics.

Unraveling the recruitment problem: A review of environmentally-informed forecasting and management strategy evaluation

Studies describing and hypothesizing the impact of climate change and environmental processes on vital rates of fish stocks are increasing in frequency, and concomitant with that is interest in incorporating these processes in fish stock assessments and forecasting models. Previous research suggests that including environmental drivers of fish recruitment in forecasting is of limited value, concluding that forecasting improvements are minimal while potential spurious relationships were sufficient to advise against inclusion. This review evaluates progress in implementing environmental factors in stock-recruitment projections and Management Strategy Evaluations (MSEs), from the year 2000 through 2017, by reviewing studies that incorporate environmental processes into recruitment forecasting, full-cycle MSEs, or simulations investigating harvest control rules. The only successes identified were for species with a short pre-recruit survival window (e.g., opportunistic life-history strategy), where the abbreviated life-span made it easier to identify one or a limited set of key drivers that directly impact dynamics. Autoregressive methods appeared to perform as well, if not better, for species with a longer pre-recruit survival window (e.g., seasonal, inter-annual) during which the environment could potentially exert influence. This review suggests that the inclusion of environmental drivers into assessments and forecasting is most likely to be successful for species with short pre-recruit survival windows (e.g., squid, sardine) and for those that have bottlenecks in their life history during which the environment can exert a well-defined pressure (e.g., anadromous fishes, those reliant on nursery areas). The effects of environment may be more complicated and variable for species with a longer pre-recruit survival window, reducing the ability to quantify environment-recruitment relationships. Species with more complex early life histories and longer pre-recruit survival windows would benefit from future research that focuses on relevant species-specific spatio-temporal scales to improve mechanistic understanding of abiotic-biotic interactions.

Ecosystem‐based forecasts of recruitment in two menhaden species

AbstractGulf (Brevoortia patronus, Clupeidae) and Atlantic menhaden (Brevoortia tyrannus, Clupeidae) support large fisheries that have shown substantial variability over several decades, in part, due to dependence on annual recruitment. Nevertheless, traditional stock–recruitment relationships lack predictive power for these stocks. Current management of Atlantic menhaden explicitly treats recruitment as a random process. However, traditional methods for understanding recruitment variability carry the very specific hypothesis that the effect of adult biomass on subsequent recruitment occurs independently of other ecosystem factors such as food availability and predation. Here, we evaluate the predictability of menhaden recruitment using a model‐free approach that is not restricted by these strong assumptions. We find that menhaden recruitment is predictable, but only when allowing for interdependence of stock with other ecological factors. Moreover, while the analysis confirms the presence of environmental effects, the environment alone does not readily account for the complexity of menhaden recruitment dynamics. The findings set the stage for revisiting recruitment prediction in management and serve as an instructive example in the ongoing debate about how to best treat and understand recruitment variability across species and fisheries.

Empirically based models of oceanographic and biological influences on Pacific Herring recruitment in Prince William Sound

Abundances of small pelagic fish can change dramatically over time and are difficult to forecast, partially due to variable numbers of fish that annually mature and recruit to the spawning population. Recruitment strength of age-3 Pacific Herring (Clupea pallasii) in Prince William Sound, Alaska, is estimated in an age-structured model framework as a function of spawning stock biomass via a Ricker stock-recruitment model, and forecasted using the 10-year median recruitment estimates. However, stock size has little influence on subsequent numbers of recruits. This study evaluated the usefulness of herring recruitment models that incorporate oceanographic and biological variables. Results indicated herring recruitment estimates were significantly improved by modifying the standard Ricker model to include an index of young-of-the-year (YOY) Walleye Pollock (Gadus chalcogrammus) abundance. The positive relationship between herring recruits-per-spawner and YOY pollock abundance has persisted through three decades, including the herring stock crash of the early 1990s. Including sea surface temperature, primary productivity, and additional predator or competitor abundances singly or in combination did not improve model performance. We suggest that synchrony of juvenile herring and pollock survival may be caused by increased abundance of their zooplankton prey, or high juvenile pollock abundance may promote prey switching and satiation of predators. Regardless of the mechanism, the relationship has practical application to herring recruitment forecasting, and serves as an example of incorporating ecosystem components into a stock assessment model.

Recruitment forecasting of yellowfin tuna in the eastern Pacific Ocean with artificial neuronal networks

The recruitment of yellowfin tuna in the eastern Pacific Ocean is modeled based on oceanographic as well as biological parameters, using two nonlinear autoregressive network models with exogenous inputs (NARX). In the first model (Model 1) the quarterly recruitment is modeled considering eastern Pacific global oceanographic conditions: the Southern Oscillation Index (SOI), the Pacific Decadal Oscillation (PDO), and spawners biomass. In Model 2, recruitment is predicted based on sea surface temperature, wind magnitude, and oceanic current magnitude of a smaller area within the eastern Pacific Ocean, considered as relevant for spawning and recruitment, and total spawners biomass. The correlation coefficient between the ANN recruitment estimate and the “real” recruitment is r>0.80 in both models. Series of sensitivity analysis suggest that the SOI and the sea surface temperature are the most important variables for the recruitment in Model 1 and Model 2 also show that warm sea surface favors recruitment. A forecasting model under different climatological scenarios indicates that the recruitment of yellowfin tuna could be higher in the period 2015–2020 compared to the ones registered in the period 2009–2013.

Chum salmon (Oncorhynchus keta) growth and temperature indices as indicators of the year-class strength of age-1 walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea

Ecosystem-based fisheries management requires the development of physical and biological time series that index ocean productivity for stock assessment and recruitment forecasts for commercially important species. As recruitment in marine fish is related to ocean condition, we developed proxies for ocean conditions based on sea surface temperature (SST) and biometric measurements of chum salmon (Oncorhynchus keta) captured in the walleye pollock (Gadus chalcogrammus) fishery in the eastern Bering Sea in three periods (July 16–30, September 1–15 and September 16–30). The main purpose of this paper was to evaluate Pacific salmon (Oncorhynchus spp.) growth as a possible indicator of ocean conditions that, in turn, may affect age-1 walleye pollock recruitment. Marine growth rates of Pacific salmon are the result of a complex interplay of physical, biological and population-based factors that fish experience as they range through oceanic habitats. These growth rates can, therefore, be viewed as indicators of recent ocean productivity. Thus, our hypothesis was that estimated intra-annual growth in body weight of immature and maturing age-4 male and female chum salmon may be used as a biological indicator of variations in rearing conditions also experienced by age-0 walleye pollock; consequently, they may be used to predict the recruitment to age-1 in walleye pollock. Summer SSTs and chum salmon growth at the end of July and September explained the largest amount of variability in walleye pollock recruitment indicating that physical and biological indices of ocean productivity can index fish recruitment.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

Modelling and forecasting stock–recruitment: current and future perspectives

AbstractThis paper presents a brief review of the present state of knowledge in stock–recruitment forecasting, including process and current methodological challenges to predicting stock–recruitment. The discussion covers the apparent inability of models to accurately forecast recruitment even when environmental covariates are included as explanatory variables. The review shows that despite the incremental success in the past hundred years, substantial challenges remain if the process of modelling and forecasting stock–recruitment is to become relevant to fisheries science and management in the next 100 years.

Predicting fish recruitment from juvenile abundance and environmental indices

Prediction of year-class strength is a critical challenge for fisheries managers. Theo- retically, predictions of recruitment should be better when they are based on estimates of cohort size taken close to the age of recruitment and may improve if the effects of environmental factors that influence pre-recruit mortality are accounted for. In practice, measurement error and difficul- ties in establishing robust recruitment-environment relationships complicate the picture. For 5 fish stocks of 4 species in 3 ecosystems, we examined the usefulness of indices of juvenile abun- dance relative to larval abundance for predicting recruitment. Further, we examined whether the use of environmental covariates improved predictions. For 2 of 4 stocks with sufficient data (1 stock did not have larval data), juvenile abundance was a better predictor of recruitment com- pared to larval indices. For the 2 other stocks, we found that juvenile indices were not superior to larval indices, possibly because of error in the measurement of juvenile abundance. In all 5 of these stocks, regression analysis showed that inclusion of environmental correlates contributed significantly to explaining recruitment variation compared to models based on juvenile indices alone. Further, cross validation showed that forecasts of future recruitment were either improved or qualitatively unchanged by including environmental correlates. This was despite apparent nonstationarity in the recruitment-environment relationships; most of the environmental vari- ables and pre-recruit abundance indices were significantly correlated with recruitment for only parts of the studied period. Such complex responses to environmental changes are difficult to anticipate, yet the environmental information should not be ignored altogether.

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of ‘state-of-the-art’ uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs.

Disease profiles differ between non-fished and fished populations of edible crab (Cancer pagurus) from a major commercial fishery

AbstractBateman, K. S., Hicks, R. J., and Stentiford, G. D. 2011. Disease profiles differ between non-fished and fished populations of edible crab (Cancer pagurus) from a major commercial fishery. – ICES Journal of Marine Science, 68: 2044–2052. Despite their significant contribution to global marine fisheries, relatively little information is available on the pathogen profile of commercially exploited decapod crustacean populations. Most of the information published relates to adult (fished) subpopulations, with almost nothing known about disease processes and mortality drivers in juveniles. The seasonal profile of pathogens in non-fished (prerecruit) and fished (recruit) subpopulations of Cancer pagurus, a major target fishery target in European waters, is investigated. Histopathology and ultrastructural assessment of tissues demonstrated a distinct pathogen profile in the two subpopulations, the apparent prevalence of specific pathogens varying with both season and life stage of the host. In some cases, highly prevalent pathogens in the prerecruit subpopulation were not observed in the recruit subpopulation. In this context, the discovery of a novel and highly prevalent haplosporidian-like parasite infecting the antennal gland and bladder of prerecruit life stages of C. pagurus is reported. Co-infections with pathogens described previously, such as Hematodinium sp. and C. pagurus bacilliform virus, were also observed. Disease assessments in the prerecruit subpopulation of commercial decapod fishery targets could perhaps be utilized to improve the estimation of cohort success and, therefore, forecasts of future recruitment to the fishery.

The impact of climate change on the performance of rebuilding strategies for overfished groundfish species of the U.S. west coast

Management Strategy Evaluation is used to evaluate the performance of management procedures based on how management decisions are made for groundfish stocks off the U.S. west coast. The analyses focus on rebuilding the stock of Pacific ocean perch to its target level when future recruitment may exhibit a long-term trend, owing to environmental forcing. Several variants of the management procedure currently used to provide scientific management advice for U.S. west coast groundfish are evaluated. The performance of variants of this management procedure which “know” (on average) the true relationship between recruitment and the environmental variable driving recruitment is not superior to that of a management procedure which bases forecasts of recruitment on average historical recruitment. Moreover, the best performance in terms of recovery rates occurs when rebuilding decisions are based on a relatively high probability of recovery.

Recruitment of striped bass in Chesapeake Bay: spatial and temporal environmental variability and availability of zooplankton prey

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 409:213-228 (2010) - DOI: https://doi.org/10.3354/meps08586 Recruitment of striped bass in Chesapeake Bay: spatial and temporal environmental variability and availability of zooplankton prey E. J. Martino1,2,*, E. D. Houde1 1University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, 1 Williams Street, Solomons, Maryland 20688, USA 2Present address: National Oceanic and Atmospheric Administration, Cooperative Oxford Laboratory, Oxford, Maryland 21654, USA *Email: ed.martino@noaa.gov ABSTRACT: Causes of recruitment variability in young-of-the-year (YOY) striped bass Morone saxatilis from Chesapeake Bay were investigated based on (1) surveys from 2001 to 2003 to document spatio-temporal variability in abundance of larval striped bass, zooplankton prey, and feeding success of larvae; (2) a synthetic analysis (1996, 1998, 1999, 2001 to 2003) to describe how environmental factors and prey affect recruitment success; and (3) a 10 yr analysis (1993 to 2002) of inter-annual differences in spatial and temporal patterns of copepods and cladocera eaten by striped bass larvae. Striped bass YOY recruitment levels varied >11-fold in the 6 years examined. In those years, mean daily freshwater flows from the Susquehanna River to the bay in March and April varied >2-fold and controlled distribution and apparent survival of striped bass larvae. Strong recruitments of YOY striped bass were associated with matches in space and time of larval striped bass and high concentrations of zooplankton prey, especially the copepod Eurytemora affinis and cladoceran Bosmina longirostris. The strongest year classes (1996, 2003) were produced in years of high freshwater flow, characterized by a high abundance of feeding-stage larvae and a spatio-temporal match between peak abundance of larvae and zooplankton prey. Enhanced feeding opportunities were most pronounced in high freshwater-flow years (1996, 1998, 2003), when larvae and zooplankton prey were strongly associated with, and apparently retained near, the estuarine turbidity maximum. First-feeding larvae fed more successfully in a high-flow year (2003; prey incidence 91%) than in a drier year (2001; prey incidence 35%). A regression model that may have forecasting potential was developed to describe recruitment of YOY striped bass for the years from 1985 to 2006. The model includes spring freshwater flow and air temperatures to predict age-0 striped bass recruitment strength (R2 = 0.65). Flow and temperature control environmental and hydrographic conditions that strongly influence spatio-temporal overlap of larval striped bass and zooplankton. The model provided accurate recruitment forecasts for 2007 and 2009, but was less successful in 2008, a year of exceptionally low recruitment. KEY WORDS: Chesapeake Bay · Striped bass · Recruitment variability · Larval fish · Zooplankton · Trophodynamics · Biophysical interactions Full text in pdf format PreviousNextCite this article as: Martino EJ, Houde ED (2010) Recruitment of striped bass in Chesapeake Bay: spatial and temporal environmental variability and availability of zooplankton prey. Mar Ecol Prog Ser 409:213-228. https://doi.org/10.3354/meps08586 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 409. Online publication date: June 23, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.

Slave to the rhythm: how large-scale climate cycles trigger herring (Clupea harengus) regeneration in the North Sea

AbstractGröger, J. P., Kruse, G. H., and Rohlf, N. 2010. Slave to the rhythm: how large-scale climate cycles trigger herring (Clupea harengus) regeneration in the North Sea. – ICES Journal of Marine Science, 67: 454–465. Understanding the causes of variability in the recruitment of marine fish stocks has been the “holy grail” of fisheries scientists for more than 100 years. Currently, debate is ongoing about the functionality and performance of traditional stock–recruitment functions used during stock assessments. Additionally, the European Commission requires European fishery scientists to apply the ecosystem approach to fisheries in part by integrating environmental knowledge into stock assessments and forecasts. Motivated to understand better the recent years of reproductive failures of commercially valuable North Sea herring, we studied large-scale climate changes in the North Atlantic Ocean and their potential effects on stock regeneration. Applying traffic light plots and time-series (TS) analyses, it was possible not only to explain the most recent reproductive failures, but also to reconstruct the full TS of recruitment from climate cycles, indexed by the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. A prognostic model was developed to provide predictions of herring stock changes several years in advance, allowing recruitment forecasts to be incorporated easily into risk assessments and management strategy evaluations, to promote a sustainable herring fishery in the North Sea. Insights gained from the analysis permit reinterpretation of the sharp decline in the North Sea herring stocks in the 1970s.