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AbstractA hierarchical Bayesian life cycle model is presented that considers spatial covariation of marine life history traits of Atlantic salmon (Salmo salar) populations in the North Atlantic. The model is based on a collective analysis of the dynamics of 13 stock units (SUs) from two continental stock groups (CSGs) in North America and Southern Europe in a single hierarchical model over the period 1971–2014. The model sets up a new assessment framework for Atlantic salmon stocks. It also provides a framework to investigate the drivers of changes in Atlantic salmon population dynamics including disentangling the effects of fisheries from those of environmental factors in a hierarchy of spatial scales. It is used to test the hypothesis of a strong spatial synchrony in marine life history dynamics of Atlantic salmon populations. The trends in two key parameters associated with the early marine phase of the life cycle are estimated: (i) the marine survival during the first summer–autumn spent at sea and (ii) the proportion of fish maturing after the first winter at sea. The results provide evidence of a decline in the marine survival together with an increase in the proportion of fish that mature after the first winter at sea, common to all SUs. Our results show an increased coherence in the covariations of trends in these two marine life history traits related to geographic proximity of SUs which support the hypothesis of a coherent response of geographically proximate Atlantic salmon populations that likely share similar migration routes.

U ovom radu, po prvi put, je iznesen status ulova u grčkom ribarstvu i to u razdoblju od 1928. do 1939. godine prema podacima iz Opće statističke službe u Grčkoj. Konkretno, izneseno je slijedeće: a) predstavljen je godišnji ulov ribarstva za sve vrste zajedno, ribolovni napor za sve vrste alata u kombinaciji i vrste specifičnih ulova tijekom razdoblja 1928.-1939., b) ponovno dodijeljive prostorne rezolucije ulova tijekom 1928.-1939., te tijekom 1964.-2007., i c) uspoređen je ulov za različita razdoblja tijekom 1928.-2007. Rezultati su pokazali da su se tijekom 1928.-1939., ulov i ribolovni napor općenito povećali. Vremenske serije svih vrsta ulova su bile izložene jakim međugodišnjim varijabilnostima, te od 40 vrsta njih 23 su pokazale značajan trend rasta. Analiza ukupnog ulova ribarstva tijekom vremena (1928.-2007.) prikazuje četiri različita uzoraka koji obilježavaju četiri faze razvoja grčkog ribarstva: 1. postupno povećavanje tijekom 1928.-1949. (pred-faza razvoja ribarstva), 2. strmo povećanje tijekom 1950.-1969. (faza rasta), 3. značajan linearni porast tijekom 1970.-1994. (faza potpunog dο prekomjernog ulova) i 4. opadajući trend tijekom 1995.-2007. (kolaps faza). Ove faze se kronološki poklapaju sa značajnim socio-ekonomskim i političkim događanjima koja su se zbivala u Grčkoj od 1928. godine. In the present study, Greek fisheries landings were extended back to 1928, for the first time, from data derived by the General Statistical Service of Greece during the 1928-1939 period. In particular, we: (a) present the annual fisheries landings for all species combined, fishing effort for all gear-types combined and species-specific landings during 1928-1939, (b) re-allocate the spatial resolution of landings during 1928-1939 to that during 1964-2007, and (c) compare the landings for different periods during 1928-2007. Results showed that during 1928-1939, landings and effort generally increased. The time series of all species landings exhibited a strong between-year variability, with 23 out of 40 species displaying a significant increasing trend. The analysis of fisheries landings over time (1928-2007) displayed four distinct patterns corresponding to four phases of Greek fisheries development: (1) a gradual increase during 1928-1949 (pre-development phase of fisheries), (2) a steeper increase during 1950-1969 (growth phase), (3) a much steeper linear increase during 1970-1994 (fully tο over-exploited phase) and (4) a declining trend during 1995-2007 (collapse phase). These phases coincided chronologically with significant socio-economic and political events that took place in Greece since 1928.

Abstract Juntunen, T., Vanhatalo, J., Peltonen, H., and Mäntyniemi, S. 2012. Bayesian spatial multispecies modelling to assess pelagic fish stocks from acoustic- and trawl-survey data. – ICES Journal of Marine Science, 69: 95–104. A Bayesian spatial model was constructed to estimate the abundance of multiple fish species in a pelagic environment. Acoustic- and trawl-survey data were combined with environmental data to predict the spatial distribution of (i) the acoustic backscattering of fish, (ii) the relative proportion of each species, and (iii) their mean length in the Gulf of Finland in the northeastern Baltic Sea. By combining the three spatial model layers, the spatial distribution of the biomass of each species was estimated. The model consists of a linear predictor on environmental variables and a spatial random effect given by a Gaussian process. A Bayesian approach is a natural choice for the task because it provides a theoretically justified means of summarizing the uncertainties from various model layers. In the study area, three species dominate pelagic waters: sprat (Sprattus sprattus), herring (Clupea harengus), and three-spined stickleback (Gasterosteus aculeatus). Results are presented for each model layer and for estimated total biomass for each species in 2 × 2 km lattices. The posterior mean and central 95% credible intervals of total biomass were sprat 45.7 kt (27.7–71.6), herring 24.6 kt (9.7–41.3), and three-spined stickleback 1.9 kt (0.9–3.2).

Abstract Independent of the effects of spawning-stock biomass (SSB), environmental variability in juvenile production, driven by factors such as temperature and food supply, have considerable potential to influence population resilience to fishing and depletion. Here, we analyse 18 time-series of Atlantic cod (Gadus morhua) stocks and empirically estimate this “environmental variability” in recruit-per-spawner (RPS) ratios. We then investigate the role of environmental recruitment variability on population resilience to fishing and ability to recover following depletion. To this end, cod population dynamics are simulated through a period of fishing, followed by a period of recovery, with alternative scenarios of recruitment variability and autocorrelation within it. The major effect of environmental recruitment variability is manifested through uncertainty. Firstly, the higher the recruitment variability, the shorter and less variable the time required for the population to decline below 15% of its carrying capacity, K. Secondly, higher variability leads to higher uncertainty in recovery time. Both these patterns are further strengthened by autocorrelation. Our findings suggest that increased environmental recruitment variability decreases resilience to fishing and increases uncertainty in recovery, thus challenging some traditional views that variability confers high productivity and rapid ability to recover from collapse.

Abstract Meta-analytic and multi-level stock–recruit analyses have traditionally focussed on the similar stock approach, but for a specific stock–recruit model. For six European herring stocks we embed both the stock and model levels within a fully Bayesian hierarchical framework, thus permitting the consideration of a wider class of models, specifically those that do not admit parameterisation via the steepness and unfished spawning potential. Model and parametric uncertainty is jointly characterised and the challenge of addressing model selection when the model itself is part of the hierarchy is addressed using the deviance information criterion (DIC) and posterior predictive analysis. For the six herring stocks the across-stock posterior evidence in favour of over-compensatory dynamics is fairly strong, with the Ricker and Shepherd models performing the best across the model-selection criteria. For a specific model form we perform a 20 year retrospective analysis (hierarchical and non-hierarchical) to see how temporal information flow occurs in a hierarchical framework, how this can improve our estimates of key parameters, and how this might influence management paradigms (such as Maximum Sustainable Yield) that are based on such estimates.

AbstractThe Law of the Sea requires that fish stocks are maintained at levels that can produce the maximum sustainable yield (MSY). However, for most fish stocks, no estimates of MSY are currently available. Here, we present a new method for estimating MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. We compare our results with 146 MSY estimates derived from full stock assessments and find excellent agreement. We present principles for fisheries management of data‐poor stocks, based only on information about catches and MSY.

Comprehensive problem framing that includes different perspectives is essential for holistic understanding of complex problems and as the first step in building models. We involved five stakeholders to frame the management problem of the Central Baltic herring fishery. By using the Bayesian belief networks (BBNs) approach, the views of the stakeholders were built into graphical influence diagrams representing variables and their dependencies. The views of the scientists involved concentrated on biological concerns, whereas the fisher, the manager, and the representative of an environmental nongovernmental organization included markets and fishing industry influences. Management measures were considered to have a relatively small impact on the development of the herring stock; their impact on socioeconomic objectives was greater. Overall, the framings by these stakeholders propose a focus on socioeconomic issues in research and management and explicitly define management objectives, not only in biological but also in social and economic terms. We find the approach an illustrative tool to structure complex issues systematically. Such a tool can be used as a forum for discussion and for decision support that explicitly includes the views of different stakeholder groups. It enables the examination of social and biological factors in one framework and facilitates bridging the gap between social and natural sciences. A benefit of the BBN approach is that the graphical model structures can be transformed into a quantitative form by inserting probabilistic information.

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future.