A Bayesian state-space size-structured population dynamics model for northern hake.

In Fisheries research bayesian state-space models have been proved to be very useful because of their capacity to include new data while measuring the uncertainty associated to biological and physical processes. The ECOKNOWS (Effective use of ecosystem and biological knowledge in Fisheries) project proposes to use bayesian analysis in a general population dynamics model GPDM as a powerful tool to include biological and physical information in management strategies. Results of a preliminary Bayesian model adapting this model to northern hake population dynamics are presented here. The model intends to reproduce as far as possible the structure and main assumptions of the current ICES model. Actually there are some parameters with convergence problems but we are developing a new version considering informative priors for the biological processes which are supposed to ameliorate the estimations.Versión de auto

A bayesian model for European hake with priors based on life history invariants.

European hake is a main target fish for the fleet operating in Atlantic waters, although there are many biological unknowns that compromise the quality of the current assessment and scientific advice. Growth, natural mortality and reproduction are main biological processes required to develop population dynamic models for assessment purposes. Currently, the parameters defining these processes such Von Bertalanffy asymptotic length, M, L 50% of maturity or Beverton-Holt steepness are estimated outside the model and set as constants. Population dynamic bayesian models provide a suitable platform to develop solutions to this kind of problems since Bayesian models allow using additional knowledge from similar species in the form of the priors. Informative priors for biological key parameters have been developed based on available data for Northern hake combined with information from other hake species all over the world. This information is analyzed on the light of ecological theory for life history invariants (LHI) to produce the required priors. LHI figures are relatively constant among similar species. Information from other hakes may help to fill the gap in assessment and management of European hake. The pros and cons of using these informative priors to improve hake assessment are finally discussed

State-of-the-art of historical earthquake research in Fennoscandia and the Baltic Republics

We review historical earthquake research in Northern Europe. 'Historical' is defined as being identical with seismic events occurring in the pre-instrumental and early instrumental periods between 1073 and the mid-1960s. The first seismographs in this region were installed in Uppsala, Sweden and Bergen, Norway in 1904-1905, but these mechanical pendulum instruments were broad band and amplification factors were modest at around 500. Until the 1960s few modern short period electromagnetic seismographs were deployed. Scientific earthquake studies in this region began during the first decades of the 1800s, while the systematic use of macroseismic questionnaires commenced at the end of that century. Basic research efforts have vigorously been pursued from the 1970s onwards because of the mandatory seismic risk studies for commissioning nuclear power plants in Sweden, Finland, NW Russia, Kola and installations of huge oil platforms in the North Sea. The most comprehensive earthquake database currently available for Northern Europe is the FENCAT catalogue covering about six centuries and representing the accumulation of work conducted by many scientists during the last 200 years. This catalogue is given in parametric form, while original macroseismic observations and intensity maps for the largest earthquakes can be found in various national publications, often in local languages. No database giving intensity data points exists in computerized form for the region. The FENCAT catalogue still contains some spurious events of various kinds but more serious are some recent claims that some of the presumed largest historical earthquakes have been assigned too large magnitude values, which would have implications for earthquake hazard levels implemented in national building codes. We discuss future cooperative measures such as establishing macroseismic data archives as a means for promoting further research on historical earthquakes in Northern Europe

Effective use of ecosystem and biological knowledge in fisheries

The general aim of the ECOKNOWS project is to improve the use of biological knowledge in fisheries science and management. The lack of appropriate calculus methods and fear of statistical overparameterisation has limited biological reality in fisheries models. This reduces biological credibility perceived by many stakeholders. We solve this technical estimation problem by using up-to date methodology supporting more effective use of data. The models suggested will include important knowledge about biological processes and the applied statistical inference methods allow to integrate and update this knowledge in stock assessment. We will use the basic biological data (such as growth, maturity, fecundity, maximum age and recruitment data sets) to estimate general probabilistic dependencies in fish stock assessments. In particular, we will seek to improve the use of large existing biological and environmental databases, published papers and survey data sets provided by EU data collection regulations and stored by ICES and EU member countries. Bayesian inference will form the methodological backbone of the project and will enable realistic estimations of uncertainty. We develop a computational learning approach that builds on the extensive information present in FishBase (www.fishbase.org).The developed methodology will be of fundamental importance, especially for the implementation of the Ecosystem Approach to Fisheries Management. It has been a difficult challenge even for target species with long data series, and now the same challenge is given for new and poorly studied species. We will improve ways to find generic and understandable biological reference points, such as the required number of spawning times per fish, which also supports the management needs in the developing countries. ECOKNOWS applies decision analysis and bioeconomic methods to evaluate the validity and utility of improved information, helping to plan efficient EU data collection.North Atlantic (NA) stock assessments address the marine phase, estimating returns to home waters, with Pre-Fishery Abundance (PFA) estimated through raising of national (or regional) annual catches by exploitation rates and attributing unreported catch and natural mortality ranges in Monte Carlo simulations. Baltic stocks in contrast, are estimated through integrated Bayesian life cycle state-space models including riverine and sea phases (Michielsens et al., 2008). There is presently no interaction between the two methodologies.We detail the two approaches specifying similarities in biology, as a prerequisite to their harmonization for parallel inference and risk analysis, independent of scales, available data and management objectives. Through aggregations of scale and availability, assimilations of data differ. For the Baltic much is performed within the forecasting framework, and while aggregations in the NA case are disparate, finer scale details are available. In the Baltic a scale of “river” is used as the geographical unit, while in the NA, 3 geo-regions are treated independently, each operating at arbitrary regional scales. To harmonize NA and Baltic approaches, a multi-scale integrated life cycle model in a Hierarchical Bayesian Modelling (HBM) framework is proposed for the NA to capture inherent complexities from mixing of life cycle age and stage cohorts, which is currently not addressed. A stage-structured life cycle approach is proposed, incorporating freshwater and marine phase variability of life histories (survival and life history choices) and auto-regenerated cohort dynamics. This represents a large change in both the modelling and statistical inference framework.Key structural hypotheses and common informative prior distributions for modelling demographic processes, for both NA and Baltic models are developed. Together with the Bayesian methodology these form the core of the harmonization process. To harmonize modelling of the demographic process the following items are necessary: State-space representation of all life stages including those not directly observed to explicitly separate out modeling of the demographic and observation processes, so as the harmonization of the models for the core ecological process can be thought independently from the data availability. Age/stage-based demographic models to integrate biological and ecological knowledge of population dynamics, characterized by seaward migrations of smolts and spawning migration of adults back to freshwater, accommodating intra- and inter-population variability in life history traits. Probabilistic demographic transitions and between-years variability of certain parameters to capture both environmental and demographic stochasticity. Variable egg to juvenile density-dependent average survival, of classical survival functions. Common approach to forecast yearly variations of marine post-smolts survival.Funer: FP7-KBBE European Commission CORDI