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International audience; Climate change by altering spatio-temporal distributions of suitable habitats leads to modifications in a multitude of species ranges. In recent years, the ability of species to physically adjust to the changing climatic conditions is of growing concern. We developed a generic trait-based method to assess the ability of species to explore new suitable habitats under climate change. Anadromous fish populations of the northern Atlantic were chosen as a suitable application case due to their high variability in terms of migratory behavior and degree of philopatry. The calculation of an Exploratory Potential Index (EPI) relies on a formal framework and a participatory procedure adapted from the Analytical Hierarchy Process (AHP) theory. It combines independent relevant life-history traits and ecological attributes into a single numeric value and permit to gather expert opinion by decomposing a complex issue into a hierarchy of more easily comprehended sub-problems through pairwise comparison matrices. Straying, earliness of first maturity, body size at first maturity, Distance covered to access feeding grounds and the reproduction strategies were identified by the expert. Results allow to identify different dispersal strategies an support strong differences in the ability to explore potential suitable habitat among anadromous fish. Even though they have contrasted life cycles, Morone saxatalis, Acipenser oxyrinchus and Alosa mediocris outperformed most of the species, while other species as Acipenser brevirostrum demonstrated had the lowest scores. Broader applications of the EPI would help to identify regions acting as potential sources of migrants and thus of priority for conservation biologists and resources.

International audience; This poster was presented during the AFH conference in June 2015. It presents the methodology based on AHP developped. It aims to estimate an Exploratory Potential Index for anadromous fish of the north atlantic Ocean. It combines several independant variables and experts opinion in order to provide a single composite value.

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 CORDIS

Bayesian modelling of the dynamics of complex of salmon populations in the North Atlantic

La gestion au rendement maximal durable : quel schema de transition pour les pêcheries bretonnes ?