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|Title: ||Effective use of ecosystem and biological knowledge in fisheries|
|Authors: ||Rivot, E.|
|Issue Date: ||2013|
|Series/Report no.: ||ECOKNOWS;244706|
|Abstract: ||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.|
|Description: ||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.|
|Appears in Collections:||EU Framework Research|
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