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Nearshore habitats are essential for many marine fish species but are subject to anthropogenic stressors. Assessing the consequences of essential fish habitat degradation on population dynamics and productivity is challenging. We address this by focusing on a metapopulation of the common sole Solea solea, a high-value, exploited flatfish in the Eastern English Channel (EEC). Multidisciplinary data and expert knowledge were compiled to build feasible restoration scenarios for the availability of suitable habitat (measured in habitat surface extent) and chemical quality of juvenile habitats in the highly anthropized Seine estuary, the largest estuary and potentially a sole nursery area of primary interest in the EEC. Scenarios were simulated with a spatially structured life-cycle model to investigate the consequences of local restoration on restricted nursery habitats in the estuary. Restoring surface extent and habitat quality in the Seine estuary dramatically enhances spawning stock biomass (+18%) and fishery catches (+13%) in the EEC. Restoring habitat quality has a greater effect than restoring habitat surface. Because of the low connectivity between subpopulations of sole in the EEC, most of the local restoration benefits remain regional and affect the subpopulation that directly depends on the Seine nursery, with only moderate spread to the entire EEC. Our study emphasizes the utility of spatial simulation models for integrating multidisciplinary knowledge and assessing the consequences of local anthropogenic pressures at wider metapopulation scales. We provide a means of building robust methods to assess the benefits of nearshore habitat restoration for enhancing fish populations and fisheries and integrate habitat value into the sustainable management of exploited species.

Anthropogenic pressures have resulted in declines in diadromous fish. Many diadromous fish which were commercially important are now threatened and protected. Little is known about their marine life history phases, and no observation-based Species Distribution Model exists for this group of species at sea. Yet, fisheries dependent and independent data could provide new insights into the distribution of diadromous fish at sea.We collated a database of 168 904 hauls from fisheries observer bycatch data and scientific fisheries surveys, from eastern Atlantic and Mediterranean waters. The distribution of eleven rare and data-poor diadromous fish (shads, lampreys, salmonids, the European eel, the thinlip mullet, smelt and the European flounder) were modelled. A Bayesian site occupancy model, that incorporates imperfect detection to account for repeat detections and non-detections, the non-random nature of fishing gear type and spatial autocorrelation was used. From the model outputs, we explored bycatch risk and the role of MPAs, required under the Marine Strategy Framework Directive and Habitat Directive and assessed.Diadromous fish were observed within relatively shallow coastal areas. Species specific gear bycatch trends were observed. Core distribution areas corresponded to their known water basin presence, indicating connectivity with their freshwater habitats. Numerous Habitat Directive Marine Protected Areas were found to be of relevance.Given the coastal distribution of these species, they are exposed to higher anthropogenic pressures from both terrestrial and marine environments. Risk of bycatch at sea for most species appears to be low. Nonetheless, for threatened individuals, even a small amount of bycatch may impact their populations, especially since misreporting is likely to be high. Differences in catchability between gears highlight potential benefits of limiting access of certain gears within protected areas to reduce bycatch.

While, quantifying trophic interactions is a critical path to understanding and forecasting ecosystem functioning, fitting trophic models to field data remains challenging. It requires flexible statistical tools to combine different sources of information from the literature and fieldwork samples. We present EcoDiet, a hierarchical Bayesian modeling framework to simultaneously estimate food-web topology and diet composition of all consumers in the food web, by combining (i) a priori knowledge from the literature on both food-web topology and diet proportions; (ii) stomach content analyses, with frequencies of prey occurrence used as the primary source of data to update the prior knowledge on the topological food-web structure; (iii) and biotracers data through a mixing model (MM). Inferences are derived in a Bayesian probabilistic rationale that provides a formal way to incorporate prior information and quantifies uncertainty around both the topological structure of the food web and the dietary proportions. EcoDiet was implemented as an open-source R package, providing a user-friendly interface to execute the model as well as examples and guidelines to familiarize with its use. We used simulated data to demonstrate the benefits of EcoDiet and how the framework can improve inferences on diet matrix by comparison with classical network MM. We applied EcoDiet to the Celtic Sea ecosystem, and showed how combining multiple data types within an integrated approach provides a more robust and holistic picture of the food-web topology and diet matrices than the literature or classical MM approach alone. EcoDiet has the potential to become a reference method for building diet matrices as a preliminary step of ecosystem modeling and to improve our understanding of prey-predator interactions.

Populations of Atlantic salmon Salmo salar have experienced precipitous declines in abundance since the 1970s. This decline has been associated with reduced numbers of adult salmon returning to freshwater from their marine migration, i.e., their marine return rates (MRR). Thus, understanding the factors that affect MRR is of crucial conservation importance. We used a state-space model with a 13-year time series of individually tagged salmon mark-recapture histories on the River Frome, southern England, to test the effect of smolt body length on their MRR. As well as smolt length, the model tested for the influence of environmental covariates that were representative of the conditions experienced by the smolts in the early stages of their seaward migration, i.e., from the lower river to the estuary exit. The model indicated that, even when accounting for environmental covariates, smolt body length was an important predictor of MRR. While larger smolts have a higher probability of returning to their natal river as adults than smaller smolts, and one-sea-winter salmon have a survival rate twice as high as multi-sea-winter salmon, the actual biological mechanisms underpinning this phenomenon remain uncertain. These results have important applications for salmon conservation, as efforts to bolster salmon populations in the freshwater environment should consider ways to improve smolt quality (i.e., body size) as well as smolt quantity. This article is protected by copyright. All rights reserved.

Data and knowledge mobilisation are significant challenges in ecology and resource management, with the journey from data collection through to management action often left incomplete due to difficulties sharing information across diverse and dispersed communities. This disconnect between science and management must be resolved if we are to successfully tackle the increasing impact of human activity on our ecosystems. Across their North Atlantic range, Atlantic salmon (Salmo salar L.) populations are in steep decline in many areas and urgent actions are required to curb this decline. Being commercially important this species has been subject to intense research, but management action often suffers from both a lack of access to this knowledge resource and support for its integration into effective management strategies. To respond to this challenge, the science and management communities must place higher priority on mobilising existing and emerging knowledge sources to inform current and future resource use and mitigation strategies. This approach requires a more complete picture of the current salmon ecology data and knowledge landscape, new mechanisms to enable data mobilisation and re-use, and new research to describe and parameterise the responses of wild populations to habitat changes. Here we present a unique interface for registering and linking data resources relevant to the Atlantic salmon life cycle that can address the data mobilisation aspect of these challenges. The Salmon Ecosystem Data Hub is a salmon-specific metadata catalogue, natively interoperable with many existing data portals, which creates a low resistance pathway to maximise visibility of data relevant to Atlantic salmon. This includes the capacity to annotate datasets with life-stage domains and variable classes, thereby permitting dispersed data to be formally contextualised and integrated to support hypotheses specific to scenario-based modelling and decision-making. The alignment and mobilisation of data within the Salmon Ecosystem Data Hub will help advance the development of appropriate environmentally driven forecast models and an ecosystem-based management approach for Atlantic salmon that optimises future management strategies.

This study provides new data on Atlantic salmon Salmo salar life-history traits across France. Using a long-term recreational angling database (1987-2013) covering 34 rivers in three regions (genetic units), a decline in individual length, mass and a delayed adult return to French rivers was reported. Temporal similarities in trait variations between regions may be attributed to common change in environmental conditions at sea. The relative rate of change in phenotypic traits was more pronounced in early maturing fish [1 sea-winter (1SW) fish] than in late maturing fish (2SW fish). Such contrasted response within populations highlights the need to account for the diversity in life histories when exploring mechanisms of phenotypic change in S. salar. Such detailed life-history data on returning S. salar have not previously been reported from France. This study on French populations also contributes to reducing the gap in knowledge by providing further empirical evidence of a global pattern in S. salar across its distribution range. Results are consistent with the hypothesis that the observed changes in life-history traits are primarily associated with environmental changes in the North Atlantic Ocean. They also emphasize the presence of less important, but still significant contrasts between region and life history.

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. Copyright © 2014 John Wiley & Sons, Ltd.

Recruitment success in marine species is mostly driven by the high and variable mortality of first life stages, and the relationships between stock and recruitment are then largely dominated by residual variability. We show that analysing the residual variability may provide insights on the density-dependence process occurring during the recruitment. Following the seminal formulation of Minto et al. (Survival variability and population density in fish populations. Nature, 2008), we show that when recruitment is considered as a sequence of a pelagic stage with stochastic density-independent mortality followed by a second stage with stochastic density-dependent mortality, then the variability of the recruitment rate per spawning biomass (RPSB) should be a decreasing function of the spawning biomass. Using stock–recruit data of 148 stocks from the RAM legacy database, we provide a test of this hypothesis by showing that the variability of RPSB is lower for fish species with the higher concentration during juvenile stages. Second, a hierarchical Bayesian model (HBM) is built to derive a meta-analysis of stock–recruit data for 39 flatfish stocks, characterized by a high concentration of juveniles in coastal nursery habitats. Results of the HBM show that the variance of the RPSB decreases with the spawning biomass for almost all stocks, thus providing strong evidence of density-dependence during the recruitment process. Finally, we attempt to relate patterns in recruitment variance to relevant life-history traits of flatfish species.

Bayesian observation error (OEM), process error (PEM) and state-space (SSM) implementations of a Fox biomass dynamics model are compared using a simulation-estimation approach and by applying them to data for the octopus fishery off Mauritania. Estimation performance is evaluated in terms of bias, precision, and reliability measured by the extreme tail-area probability and the mean highest posterior density interval. The PEM generally performs poorest of the three methods in terms of the these performance metrics. In contrast, the OEM is precise, but under-represents uncertainty. The OEM is outperformed by the SSM in terms of its ability to provide posterior distributions which adequately capture parameter uncertainty. It is key to consider the above four metrics when comparing estimation performance in a Bayesian context. Finally, although model performance measures are useful, there is still a need to examine goodness of fit statistics in actual applications.

For many marine fish species, recruitment is strongly related to larval survival and dispersal to nursery areas. Simulating larval drift should help assessing the sensitivity of recruitment variability to early life history. An individual-based model (IBM) coupled to a hydrodynamic model was used to simulate common sole larval supply from spawning areas to coastal and estuarine nursery grounds at the population scale in the eastern Channel on a 14-yr time series, from 1991 to 2004. The IBM allowed each particle released to be transported by currents from the hydrodynamic model, to grow with temperature, to migrate vertically giving stage development, and possibly to die according to drift duration, representing the life history from spawning to metamorphosis. Despite sensitivity to the larval mortality rate, the model provided realistic simulations of cohort decline and spatio-temporal variability of larval supply. The model outputs were analysed to explore the effects of hydrodynamics and life history on the interannual variability of settled sole larvae in coastal nurseries. Different hypotheses of the spawning spatial distribution were also tested, comparing homogeneous egg distribution to observation and potential larval survival (PLS) maps. The sensitivity analyses demonstrated that larval supply is more sensitive to the life history along larval drift than to the phenology and volume of spawning, providing explanations for the lack of significant stock–recruitment relationship. Nevertheless, larval supply is sensitive to spawning distribution. Results also suggested a very low connectivity between supposed different sub-populations in the eastern Channel.

P>1. Despite carrying capacity being one of the most important parameters in population management and modelling, we lack substantial evidence for habitat limitations on freshwater species. Here we tested the ideal free distribution (IFD) hypothesis using an indirect behaviour-based method for small closed populations assuming that animals can effectively estimate habitat suitability and distribute themselves accordingly in time and space. 2. We analysed spatiotemporal variations in the density of the European eel Anguilla, a catadromous species with good colonisation abilities in a small coastal catchment in France. The general linear model used enabled us to test simultaneously the effect of temporal, macro- and meso-scale habitat factors on the presence and abundance of eels at 30 sites over an 8-year period. 3. Almost every site sampled had eels, whatever its location on the catchment and its habitat characteristics. Density estimates (overall mean +/- SD of 0.40 +/- 0.48 m-2) were at the upper range of other values for European catchments. Moreover, eel densities were mainly influenced by the availability of suitable habitats (rocky substratum and instream cover), which suggests that their distribution reflects an IFD. 4. Despite marked variability in recruitment, the density of the oldest size-class remained stable over the study, suggesting that density-dependent mortality occurred, probably due to intraspecific competition for space and food and to predation. 5. These findings suggest that eel habitats are saturated in the Fremur. Therefore, we suggest that the mean abundance of eels observed could serve as a threshold value for other male-dominated river stocks (provided they have a similar overall percentage of suitable habitats) that are common in small, low gradient streams on the north-Atlantic coast of Europe.

Understanding and modelling insect pest dispersal is an important prerequisite for designing integrated pest management programs. Nevertheless, studies investigating the dispersal of small insects in natural conditions remain scarce mainly because of the difficulty of tracking the movements of these organisms. Here we propose to use Hierarchical Bayesian Modelling (HBM) framework to gain knowledge on hidden processes that cannot be observed directly in natura, such as insect landing and insect mortality, through the definition of latent variables. An HBM describing crop colonization by winged aphids was fitted to a large dataset of field observations issued from a long term survey at a wide scale of both aerial and field densities of the bird cherry-oat aphid Rhopalosiphum padi. This study provides the first evidence that suction trap data are reliable proxies of aphid colonizing rates in cereal fields in autumn and can be a nice alternative to the very time-consuming crop sampling. The proportion of winged aphids landing in cereal fields is shown to vary between regions according to the degree of investment of local R. padi population in sexual reproduction. Results also indicate that under autumnal field conditions, less than 5% of winged aphids survive more than 10 days after landing. This HBM provides the basis of a predictive model for aphid crop colonization that fully accounts for all sources of uncertainty. It should be of great value to improve the trust of users in any decision making systems.

Oceanic larvae of the European (Anguilla anguilla) and American (A. rostrata) eels have to cross the Atlantic Ocean from the Sargasso Sea to European or North American coasts before entering continental habitats. In some European rivers, eel recruitment is now

International audience; The migration duration of European eel (Anguilla anguilla) larvae (leptocephali) from the spawning areas in the Sargasso Sea to the European continental shelf remains highly controversial, with estimates varying from 6 months to more than 2 yr. We estimated the fastest migration period and the shortest distance travelled by eel larvae by simulating Lagrangian particles released in the Sargasso Sea and by simulating a range of larval behaviours (fixed-depth drift, vertical diurnal migration and active-depth selection to maximize current velocity). This enabled us to compute (i) a passive drift speed, and (ii) a hypothetic swimming speed needed for European eel larvae to cross the Atlantic in 6 months (i.e., the migration duration estimated from otolith daily growth increments). Our results show that the minimum travel time for an eel larva that is passively drifting was 10 months and 3 days. Active behaviours (vertical diurnal migration and rheotaxis) paradoxically increased the migration period. We found that for leptocephali to cross the Atlantic Ocean in 6 months, they would need to swim a minimum of 3.4 body lengths per second for 8200 km. No larvae have been observed with such swimming capabilities. These results provide evidence that leptocephali cannot cross the Atlantic in 6 months.

European eel decline is now widely observed and involves a large number of factors such as overfishing, pollution, habitat loss, dam construction, river obstruction, parasitism and environmental changes. In the present study, we analysed the influence of environmental conditions in the Sargasso Sea and Atlantic ocean circulation on European glass eel recruitment success. Over a recent 11-year period, we showed a strong positive correlation between an original index of glass eel recruitment and primary production in eel spawning area. Moreover, primary production was negatively correlated with temperature in the Sargasso Sea. Therefore, we used sea temperature as an inverse proxy of marine production. A close negative relationship has been found over the last four decades between long-term fluctuations in recruitment and in sea temperature. These findings were reinforced by the detection of a regime shift in sea temperature that preceded the start of the decline in glass eel recruitment in the early 1980s. By contrast, variations in integrative indices measuring ocean circulation, i.e.\ latitude and strength of the Gulf Stream, did not seem to explain variations in glass eel recruitment. Our results support the hypothesis of a strong bottom-up control of leptocephali survival and growth by primary production in the Sargasso Sea on short and long time-scales. We argue that sea warming in the eel spawning area since the early 1980s has modified marine production and eventually affected the survival rate of European eels at early life stages.

We present a Bayesian approach of a Ricker stock-recruitment (S/R) analysis accounting for measurement errors on S/R data. We assess the sensitivity of posterior inferences to (i) the choice of Ricker model parameterizations, with special regards to management-related ones, and (ii) prior parameter distributions. Closed forms for Ricker parameter posterior distributions exist given S/R data known without error. We use this property to develop a procedure based on the Rao–Blackwell formula. This procedure achieves integration of measurement errors by averaging these closed forms over possible S/R data sets sampled from distributions derived from a stochastic model relating field data to the S and R variables. High-quality Bayesian estimates are obtained. The analysis of the influence of different parameterizations and of the priors is made easier. We illustrate our methodological approach by a case study of Atlantic salmon (Salmo salar). Posterior distributions for S and R are computed from a mark–recapture stochastic model. Ignoring measurement errors underestimates parameter uncertainty and overestimates both stock productivity and density dependence. We warn against using management-related parameterizations because it makes the strong prior assumption of long-term sustainability of stocks. Posterior inferences are sensitive to the choice of prior. The use of informative priors as a remedy is discussed.

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.

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.