Contribution of catastrophic events to stochasticity of the recruitment process: an application to Atlantic salmon

RésuméInternational audienc

Spatial synchrony in the response of a long range migratory species ( Salmo salar ) to climate change in the North Atlantic Ocean

International audienceA major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock-groupings (CSG) in North America (NA) and Southern Europe (SE) over the period 1971-2014. We found strong coherence in the temporal variation in post-smolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor 1.8 over the 1971-2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the post-smolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for NA populations 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for SE populations 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats

Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock

Publisher's version (útgefin grein)The biological status of many commercially exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatiotemporal dynamics of such species can lead to new insights into population processes and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent data sets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple data sets allowed us to cover the entire distribution of the northern population of M. surmuletus, exploring dynamics at different spatiotemporal scales and identifying key environmental drivers (i.e., sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and (or) observation uncertainty is high, or when data are sparse, if we combine multiple data sets within a hierarchical modelling framework, accurate and useful spatial predictions can still be made.CP’s postdoc was funded by Ifremer and France Filière Peche. The authors thank Bruno Ernande for suggestions and comments that improved the work during the analysis. The authors also thank two anonymous reviewers for their comments, which helped to improve the manuscript.Peer Reviewe

Fishery-Based indicators of Management Impact: Assessing Relevance and Robustness Using A Bio-Economic Simulation Model

Indicators are widely used in fisheries management decision process as measures for monitoring ecosystem status or management performance. In the current context, the shift to an ecosystem-based approach to fisheries management requires to complete usual population or community indicators with indicators reflecting state and dynamics of fishing activity as well as economic viability. However, it is difficult for decision makers to select relevant indicators among the numerous existing ones. In addition, the complexity of the mechanisms involved makes the unequivocal interpretation of indicator variations seldom possible using only statistical models. We propose to resort to the ISIS-Fish fishery dynamics model to select robust and relevant fishery indicators. ISIS-Fish population submodel was set up to reproduce major processes of anchovy population dynamics in space and time and surplus production models were used for the other target species of the fishery. To take fishermen behavior into account while modeling fleet dynamics, we considered a discrete choice model using economical interest in the different possible métiers, fishers habits and management constraints within a random utility modeling framework. Applying sensitivity analysis methods, simulation designs were built crossing a variety of management scenarios and uncertainty hypotheses. Consequently, a list of resulting candidate metrics referring both to population and fishing activities were computed and statistically analyzed. Metrics that were the most sensitive to management and the most robust to uncertainties were considered to be relevant and selected for assessing management impact on the fishery

Best practices for the provision of prior information for Bayesian stock assessment

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future

The Second ICES/NASCO Workshop on Salmon Mortality at Sea (WKSalmon2; outputs from 2022 meeting) The Second ICES/NASCO Workshop on Salmon Mortality at Sea (WKSalmon2; outputs from 2022 meeting)

ICES, in consultation with the North Atlantic Salmon Conservation Organisation (NASCO), convened a series of workshops to explore how to use biological and environmental data in models to advance the conservation of wild Atlantic salmon (<em>Salmo salar</em> L.) at sea. This workshop set out to consider multiple candidate hypotheses contributing to changes in the temporal patterns of abundance, and agree the priority research questions. No agreement on the development of a set of priority marine mortality hypotheses was reached. This resulted from the recognition of the hierarchical nature of ecosystem controls, and important complexities introduced by evolutionary diversity. An integrated ecological-evolutionary framework was proposed for the evaluation of hypotheses, and to identify key points in space and time. There was an agreed need for the continuation of cooperative initiatives to examine drivers of marine growth change using standardised approaches, and in the evolutionary delineation of stock units. These were seen as productive pathways to significantly enhance understanding of the marine factors impacting species abundance. The workshop recognised that options for developing and testing hypotheses remain constrained by the availability and quality of data, and identified ways to mobilise existing knowledge resources on key aspects of salmon ocean ecology. These focused on the synthesis of physical ocean data and model outputs, involving ocean basin-wide evaluations of available energy from surveys of lower trophic levels, and updating of population-specific biological information. The workshop agreed on the need for a specific call for data from pelagic commercial fisheries, given the broad scale of this activity and potential overlap with salmon migrations. There was also the recognition that Atlantic salmon should be included in the ICES Working Group on Bycatch of Protected Species (WGBYC) Protected, Endangered and Threatened Species list. Much of the work required to mobilise useful data sources was recognised as being outside the scope of existing ICES data calls, or the constituted core work of ICES Working Group on North Atlantic Salmon (WGNAS). Recommendations for the third workshop are for 1. More detailed consideration of how to access the work needed for data mobilisation, and 2. The identification of well-defined, achievable outcomes

Application des modèles graphiques bayesiens à la modélisation, l'inférence et la prédiction en écologie appliquée

Application des modèles graphiques bayesiens à la modélisation, l'inférence et la prédiction en écologie appliquée. Journées Statistiques et Application en Biologi

Ecological issues and hierarchical bayesian modelling techniques : case studies from fisheries ecology

International audienc

Revisiting conservation limits for Atlantic salmon: a new risk based definition

International audienc