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Rougier, T., Lambert, P., Drouineau, H., Girardin, M., Castelnaud, G., Carry, L., Aprahamian, M., Rivot, E., and Rochard, E. 2012. Collapse of allis shad, Alosa alosa, in the Gironde system (southwest France): environmental change, fishing mortality, or Allee effect? – ICES Journal of Marine Science, 69: 1802–1811. At the end of the 20th century the allis shad population in the Gironde was the largest in Europe. During the first decade of the 21st century, catches declined dramatically by two orders of magnitude, and a fishery moratorium was implemented in 2008. This deterioration in the status of the stock was confirmed by three independent assessments (abundance of juveniles and of potential and effective spawners). Three hypotheses on the cause of the collapse were examined: (i) an environmental change in freshwater and/or in the estuary; (ii) an increase in marine and/or estuarine mortality; and (iii) the presence of an Allee effect. Changes in flow, temperature, and water quality over the period were inconclusive, but remain a possible causative factor. The instantaneous rate of marine (mean: 0.7 year−1, s.e. 0.1 year−1) and estuarine (mean: 2.6 year−1, s.e. 0.1 year−1) mortalities showed no trend between 1991 and 2003. Nevertheless, the past high estuarine (fishing) mortalities combined with a demographic Allee effect in the reproduction dynamics could explain the population collapse and hamper the stock recovery under the moratorium. This conclusion is, however, tentative as it was not possible to prove the presence of the density-dependent mechanism associated with the demographic Allee effect.

http://www.crcpress.com/product/isbn/9781584889199; making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors' website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

A von Bertalanffy growth model for young-of the-year Atlantic salmon Salmo salar in a small French coastal stream was fitted using water temperatures and densities of juvenile salmonids (S. salar and brown trout Salmo trutta) as covariates influencing daily growth rate. The Bayesian framework was used as a template to integrate prior information from external data sets. The relative influence of the covariates on parr growth was quantified and results showed that growth of S. salar juveniles depended on both water temperatures and densities, but that most of the spatiotemporal variability of growth resulted from local spatiotemporal variations of 0+ age salmonid (S. salar and S. trutta) densities. Further analysis revealed that the fluctuations in young-of-the-year salmonid densities are likely to dominate the effects of potential future warming of water temperature due to climate change. It is concluded that factors that could affect salmonid densities might well have a greater effect on S. salar population dynamics than factors influencing water temperatures.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]QUASAREViewpoint article; We, members of the Association Francaise d'Halieumetrie, share the conviction that European fisheries have a future. It is time to get off the wrong track. Radical change is required to embark on the path of sustainable development and truly implement the ecosystem approach to fisheries. Today, high fishing pressure is deployed to catch a rare resource, which in return is responsible for the rarity. We must reverse the situation: an abundant resource that affords high catches applying moderate fishing pressure. All management tools and especially all actors must be mobilized. The transition is not simple, and European solidarity must accompany the changes. It is well worth the effort. By reducing fishing pressure, it will be possible in the medium term to restore fish stocks, to make ecosystems return to good ecological status, to stabilize and even increase catches and to considerably improve economic efficiency. This is obviously in the interest of fishers but it is also in the interest of future generations and all users of this common heritage that is the sea. (C) 2011 Elsevier B.V. All rights reserved.

Sp. Iss. SI Proceedings of the Seventh International Symposium on Flatfish Ecology, Part I; Estuaries and coastal waters are essential nursery habitats for many marine species, and especially for flatfishes. Thus, investigating how anthropogenic disturbances affect the quality of these habitats is of major importance to understand their consequences on the population renewal of marine species. The aim of the present study was to analyse the effects of estuarine habitat degradation on the population of the common sole in the Eastern Channel, a key species in the fish community and fisheries in this area. We especially focused on the drastic drop in the surface area and on the low water quality of the Seine estuary, the main river of the Eastern Channel. A geographic Information System (GIS) was used to develop quantitative maps of sole nursery habitats in the Eastern Channel by using a habitat suitability model based on bathymetry and sediment structure. This approach indicated that juvenile densities are low in the Seine estuary with regards to other nursery sectors. Then, thanks to historical maps of the Seine estuary, habitat suitability maps were built for key dates in the modifications of this estuary since 1850. This backward predictive approach suggests that habitat loss in the Seine estuary has led to a 42% decrease of its nursery capacity. As the density of juvenile sole in the Seine estuary is low in comparison to other sectors, this represents only a 3% loss at the sole population scale, in the Eastern Channel. However, when we assumed that prior to anthropogenic disturbance the juvenile density in the Seine estuary might have been equivalent to the current density of adjacent sectors with higher quality, the loss in abundance could be nearly 23% (8-36%). Results suggest that the loss in habitat surface combined with habitat degradation has led to an important loss in the contribution of the Seine estuary nursery to the whole sole population in the Eastern Channel. (C) 2009 Elsevier BM. All rights reserved.

Robert, M., Faraj, A., McAllister, M. K., and Rivot, E. 2010. Bayesian state-space modelling of the De Lury depletion model: strengths and limitations of the method, and application to the Moroccan octopus fishery. – ICES Journal of Marine Science, 67: 1272–1290. The strengths and limitations of a Bayesian state-space modelling framework are investigated for a De Lury depletion model that accommodates two recruitment pulses per year. The framework was applied to the Moroccan fishery for common octopus (Octopus vulgaris) between 1982 and 2002. To allow identifiability, natural mortality (M) and the recruitment rhythm were fixed, and the variance of both process and observation errors were assumed to be equal. A simulation–estimation (SE) approach was derived to test the performance of the method. If the data showed responses to harvest, the estimates of the most important figures, i.e. the initial abundance and the second recruitment pulse, were accurate, with relatively small bias. Results confirm that greater depletion yields smaller bias and uncertainty and that inferences are sensitive to the mis-specification of M. The 21 depletion series in the Moroccan dataset were jointly treated in a hierarchical model including random walk to capture the systematic fluctuations in estimates of catchability and initial abundance. The model provides estimates of the annual recruitment and monthly octopus population size. The recruitment estimates could be used to investigate the link between recruitment variability and the coastal North African upwelling regime to improve understanding of the dynamics and management of octopus stocks.

Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All rights reserved.

National audience; Pour la première fois, un modèle permet de prévoir les infestations de champs cultivés par un ravageur à partir de données issues de pièges à aspiration du réseau Agraphid. Il s’agit précisément des infestations d’orges d’hiver à l’automne par le puceron Rhopalosiphum padi vecteur de la JNO. Ce résultat laisse espérer pouvoir faire fonctionner divers OAD (modèles de prévision des risques liés à des ravageurs ailés voire simples seuils de traitement) à partir de données recueillies automatiquement et journellement par de tels pièges, au lieu d’exiger des données issues de comptages de terrain difficiles à fournir. Cela rendrait utilisables certains OAD négligés aujourd’hui car trop difficiles à « nourrir » et pourrait concerner d’autres espèces de pucerons, sur maïs par exemple, voire d’autres insectes. Mais pour que ces OAD, une fois étalonnés, puissent fonctionner à partir des jeux de données issues des pièges à aspiration, encore faut-il que ces pièges perdurent. Il faut donc relancer le réseau Agraphid dont seuls deux pièges fonctionnent aujourd’hui.

International audience; Using Lagrangian simulations, based on circulation models over three different hydroclimatic periods in the last 45 years in the North Atlantic Ocean, the trans-Atlantic migration of the European eel Anguilla anguilla leptocephali was simulated via the passive drift of particles released in the spawning area. Three different behaviours were modelled: drifting at fixed depth, undergoing a vertical migration or choosing the fastest currents. Simulations included mortality hypotheses to estimate a realistic mean migration duration and relative survival of A. anguilla larvae. The mean migration duration was estimated as 21 months and the mortality rate as 3·8 per year, i.e. < 0·2% of A. anguilla larvae may typically survive the trans-Atlantic migration.

International audience

Glass eel abundances are declining worldwide. This has mostly been attributed to direct impacts of human activities such as overfishing or habitat loss and degradation, whilst the potential influence of changes in oceanic conditions has received less attention. Eel are characterized by a complex and still enigmatic life cycle that includes a trans-oceanic spawning and larval migration. The apparent synchrony in the decline of eel populations worldwide suggests that the oceanic mechanisms involved are similar for all populations. We analyse the relationships between oceanic conditions in eel spawning areas and glass eel recruitment success of the 3 most commercially important species of the genus Anguilla: A. anguilla, A. rostrata, and A. japonica. We provide evidence that the survival of eel larvae is strongly correlated with food availability during their early life stages. Over the last 4 decades, changes in the marine production related to global warming may have led to the decline of European, American and Japanese eel populations. In the Pacific and Atlantic Oceans, the shifts in the temperature regime detected in the late 1970s were followed by shifts in the recruitment regime of glass eel for the 3 species. The decrease in primary production through climate-driven processes has therefore affected the recruitment of eel populations.

International audience; We present a hierarchical Bayesian modelling (HBM) framework for estimating riverine fish population size from successive removal data via electrofishing. It is applied to the estimation of the population of Atlantic salmon (Salmo salar) juveniles in the Oir River (France). The data set consists of 10 sampling sites sampled by one or two removals over a period of 20 years (1986–2005). We develop and contrast four models to assess the effect of temporal variations and habitat type on the density of fish and the probability of capture. The Bayes factor and the deviance information criterion are used to compare these models. The most credible and parsimonious model is the one that accounts for the effects of the years and the habitat type on the density of fish. It is used to extrapolate the population size in the entire river reach. This paper illustrates that HBM successfully accommodates large but sparse data sets containing poorly informative data for some units. Its conditional structure enables it to borrow strength from data-rich to data-poor units, thus improving the estimations. Predictions of the population size of the entire river reach can be derived, while accounting for all sources of uncertainty.; Nous proposons un cadre de modélisation bayésien hiérarchique (HBM) pour estimer l’abondance d’une population de juvéniles de saumon atlantique (Salmo salar) dans la rivière Oir (France) par la méthode des retraits successifs par pêche électrique. Le jeu de données est composé de 10 sites d’échantillonnage, chacun ayant été échantillonné par un ou deux passages sur une période de 20 ans (1986–2005). Quatre modèles sont développés pour introduire les variations inter-annuelles et les effets du type d’habitat sur la densité et sur la probabilité de capture. Ces modèles sont comparés à l’aide du facteur de Bayes et d’un critère d’information basé sur la déviance. Le modèle retenu est celui qui prend en compte l’effet de l’année et du type d’habitat sur la densité de juvéniles de saumons. Il est utilisé pour extrapoler la population de saumon à l’ensemble du cours d’eau. Cet article illustre que les HBM permettent de traiter des jeux de données de grande taille dont l’information portée par chaque unité échantillonnée est hétérogène. La structure conditionnelle permet d’améliorer les estimations car elle organise un transfert d’information entre les unités. Le modèle permet d’obtenir des prédictions de l’abondance sur l’ensemble du cours d’eau, tout en prenant en compte les différentes sources d’incertitude.

Dimensions du poster : 119X84cm Collation : Inconnue Dimensions du poster : 119X84cmCollation : Inconnue; Climate change may have affected growth and life history of Atlantic salmon juveniles over the last 30 years. International Symposium on Challenges for Diadromous Fishes in a Dynamic Global Environment Location

Fisheries research often involves a repetitive sampling protocol for multiple ecological units (our example is gillnetting of lakes). Estimates of abundance based on catch per unit effort can be erroneous due to lake effects on sampling efficiency. Conversely dividing data into individual lakes may lead to poor inference due to sparse data. Hierarchical Bayesian analysis compromises between these two extreme methods by estimating parameters for an individual lake, but borrowing information from other lakes. We estimated size-selective gillnet efficiency with mark-recapture data across a series of lakes subject to a constant netting effort. Hierarchical Bayesian analysis was able to prevent unrealistic selectivity functions that arose from individual lake analysis. Furthermore, the hierarchical approach was able to derive accurate parameter estimates with very few mark-recaptures in sub-sampled data trials. This paper demonstrates the hierarchical methodology for the estimation of fishery selectivity parameters. The results could be used to derive informative priors for future research which uses the proposed gillnet protocol.

International audience

This study presents the validation of a scale-reading method for age estimation of brown trout (Salmo trutta). Age estimated from the scale reading was compared with the true age from recaptured fishes pit-tagged at the 0+ stage. Recapture was performed via electrofishing or trapping during migration up to 6 years later. A sample of 375 fish was analysed, including freshwater (363 fish) with two sub-groups (resident and migratory) and anadromous forms (12 fish). Average percentage of error in the scale reading was low (6.4%) in the whole sample. Error rate dramatically increases with age. There was no error in the sea trout sample. When considering freshwater fish alone, average error rate remained low (6.6%) and was higher in resident fish (older individuals) than in migratory fish (younger individuals). No statistical difference was observed between the true age and the age estimated from scales, thus confirming the reliability of scale reading for age estimation.

International audience; Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i) an emotive simulated example, ii) application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.

The Bayesian state-space modelling framework allows us to derive inferences on stochastic stage-structured population dynamics models from multiple series of sequential observations with measurement or sampling errors. A state-space model works from the coupling of two models. The first one mimics the dynamics by conditional Markovian transitions between successive hidden states. The second model describes the observation process with random errors. Statistical inferences on states variables and parameters are easily performed via Bayesian Monte Carlo Markov chains (MCMC) methods. The flexibility of MCMC methods allows us to analyse a wide range of state-space models with non-linear relationships in the dynamic and observation equations, and non-Gaussian error structure just as well. The Bayesian paradigm is efficient for deriving quantitative diagnostics on a probability based rationale. We illustrate the value of this framework by fitting a stage-structured life cycle model for Atlantic salmon (Salmo salar) to a data set resulting from the survey of the population of the river Oir (Lower Normandy, France) between 1984 and 2001. The system dynamics includes non-linear regulation and has a probabilistic structure accommodating for both the environmental and the demographic stochasticity. The observation model consists in capture-mark-recapture experiments for the evaluation of the runs of downstream migrating juveniles (smolts) and upstream migrating spawners, and random sampling for demographic features. A full Bayesian treatment of the model is carried out by means of Gibbs sampling. Outputs of main interest consist in the joint posterior distribution of all the model parameters and state variables such as the smolt and spawner runs.

International audience; The understanding of the dynamics of fishing vessels is of great interest to characterize the spatial distribution of the fishing effort and to define sustainable fishing strategies. It is also a prerequisite for anticipating changes in fishermen's activity in reaction to management rules, economic context, or evolution of exploited resources. Analyzing the trajectories of individual vessels offers promising perspectives to describe the activity during fishing trips. A hidden Markov model with two behavioral states (steaming and fishing) is developed to infer the sequence of non‐observed fishing vessel behavior along the vessel trajectory based on Global Positioning System (GPS) records. Conditionally to the behavior, vessel velocity is modeled with an autoregressive process. The model parameters and the sequence of hidden behavioral states are estimated using an expectation–maximization algorithm, coupled with the Viterbi algorithm that captures the most credible joint sequence of hidden states. A simulation approach was performed to assess the influence of contrast between the model parameters and of the path length on the estimation performances. The model was then fitted to four original GPS tracks recorded with a time step of 15 min derived from volunteer fishing vessels operating in the Channel within the IFREMER RECOPESCA project. Results showed that the fishing activity performed influenced the estimates of the velocity process parameters. Results also suggested future inclusion of variables such as tide currents within the ecosystem approach of fisheries.

[Notes_IRSTEA]présentation power point de 17 diapos [Departement_IRSTEA]Territoires [Departement_IRSTEA]Eaux [TR1_IRSTEA]SEDYVIN [TR2_IRSTEA]QUASARE; National audience; Nous présentons ici les principales difficultés numériques rencontrées dans l'utilisation des modèles statistiques hiérarchiques en écologie et proposons l'organisation d'un groupe de travail sur le sujet.

Le Saumon atlantique (Salmo salar) compte parmi les migrateurs amphihalins les plus emblématiques. Ce poisson anadrome est présent à l’état sauvage dans tout l’Atlantique Nord, de l’ouest du Connecticut (USA) à la Baie d’Ungawa (Canada), et du nord de l’Espagne à la Norvège. Les caractéristiques particulières de son cycle de vie, notamment les stratégies de migrations entre les milieux marin et dulçaquicole, et les structures spatiales de métapopulation qui en résultent, en font un modèle écologique d’intérêt pour analyser l’effet des pressions anthropiques et environnementales à plusieurs échelles d’espace et de temps, comme la dégradation et la perte des habitats essentiels, la pêche et le changement climatique. Afin de comprendre le fort déclin des populations naturelles de saumon au cours du 20ème siècle, cette espèce a fait l’objet d’une attention scientifique particulière. Les recherches menées dans cette thèse s’inscrivent dans le cadre du projet de recherche européen FP7- ECOKNOWS et sont en lien avec les travaux du groupe de travail pour le saumon de l’Atlantique nord (ICES WGNAS). Elles poursuivent un double objectif : 1) améliorer les fondements méthodologiques du modèle d’évaluation utilisé par le groupe de travail du CIEM (WGNAS) construit à l’échelle des grands assemblages de populations pour l’aide à la décision pour la gestion de l’exploitation en mer; 2) s’appuyer sur une modélisation de la dynamique des populations à large échelle spatiale pour quantifier les variations des traits de vie de la phase marine du saumon, dans la perspective de tester l’hypothèse d’une empreinte des grands changements écosystémiques. Pour répondre à ce double objectif, nous nous sommes appuyés sur une approche de modélisation progressive. Dans un premier temps, nous avons développé un modèle de cycle de vie pour traduire la dynamique de population moyenne résultante d’un assemblage de populations. La modélisation et l’inférence sont réalisées dans le cadre des modèles hiérarchiques Bayesiens qui offrent grande flexibilité pour assimiler des sources de données et d‘informations variées dans les modèles de dynamique de population afin d’estimer les paramètres. L’application de ce modèle à l'Ecosse Est a permis de mettre en évidence un déclin des abondances en mer en lien avec une baisse de la survie lors des premiers mois de la phase marine. Les résultats montrent aussi que les taux de retour des poissons séjournant deux hivers en mer avant la maturation sexuelle ont décliné plus rapidement que ceux des poissons d’un hiver de mer. Dans un second temps, l’approche a été élargie à l’ensemble des populations de saumon de l’assemblage de populations sud-européen, afin d’explorer la cohérence des variations des traits de vie à large échelle, et ainsi tester l’hypothèse d’une réponse commune des populations de saumon à des changements écosystémiques touchant la phase marine du cycle de vie. Le modèle propose une modélisation conjointe de la dynamique des huit régions constituant l’assemblage sud-européen. Les résultats de ce modèle multi-régional mettent en évidence une forte synchronie dans les variations des paramètres de mortalité des post-smolts et dans l’âge de maturation. Les résultats sont en accord avec l’hypothèse du rôle important des conditions trophiques et/ou environnementales lors de la phase marine dans les variations de la dynamique des populations. Dans un troisième temps, nous avons proposé une méthode pour prendre en compte la variabilité interrégionale de la survie oeuf-smolt et sa composante densité-dépendante dans le modèle multi-échelle. Les résultats montrent que prendre en compte la densité dépendance dans la survie des juvéniles pendant la phase dulçaquicole du cycle de vie modifie fortement les estimations de la survie pendant la phase marine. Cela accentue la chute de la survie en mer au début des années 1990 et renforce la corrélation négative entre la survie des post-smolts pendant les premiers mois de leur phase marine et la probabilité de maturer dès la première année en mer. Ces résultats qualitatifs et quantitatifs sur la structure de la variabilité démographique au cours du cycle de vie du saumon atlantique sont discutés au regard de l’influence de différents facteurs de forçage dans une hiérarchie d’échelles spatiales.