Poor feeding opportunities and reduced condition factor for salmon post-smolts in the Northeast Atlantic Ocean

During the last few decades, many wild Atlantic salmon populations have declined dramatically. One possible contributing factor for the decline is reduced prey availability at sea. Here, we examine post-smolt diet and investigate if post-smolts show signs of selective feeding based on 2546 post-smolts sampled from west of Ireland to the northern Norwegian Sea over a 25-year period. We also test for changes over time in stomach fullness, diet, condition factor and body length. There was a clear reduction in condition factor for post-smolts sampled in the Norwegian Sea in the period 2003–2012. The post-smolt stomach fullness was also reduced in the same period. The reduction in condition factor is partly explained by reduced stomach fullness, including a reduction of highly energetic fish larvae and Amphipoda in the diet. Feeding on other prey, such as meso-zooplankton and insects, cannot substitute the high-quality fish larvae and Amphipoda in the post-smolt diet. This is the first study to document how salmon post-smolts feeding in the Norwegian Sea are affected by reduced feeding conditions. Possible causes for the observed changes in post-smolt feeding are ocean warming, decreased primary productivity, and reduced recruitment of important fish larvae.publishedVersio

The signature of fine scale local adaptation in Atlantic salmon revealed from common garden experiments in nature

Understanding the extent, scale and genetic basis of local adaptation (LA) is important for conservation and management. Its relevance in salmonids at microgeographic scales, where dispersal (and hence potential gene flow) can be substantial, has however been questioned. Here, we compare the fitness of communally reared offspring of local and foreign Atlantic salmon Salmo salar from adjacent Irish rivers and reciprocal F-1 hybrid crosses between them, in the wild home\u27 environment of the local population. Experimental groups did not differ in wild smolt output but a catastrophic flood event may have limited our ability to detect freshwater performance differences, which were evident in a previous study. Foreign parr exhibited higher, and hybrids intermediate, emigration rates from the natal stream relative to local parr, consistent with genetically based behavioural differences. Adult return rates were lower for the foreign compared to the local group. Overall lifetime success of foreigners and hybrids relative to locals was estimated at 31% and 40% (mean of both hybrid groups), respectively. The results imply a genetic basis to fitness differences among populations separated by only 50km, driven largely by variation in smolt to adult return rates. Hence even if supplementary stocking programs obtain broodstock from neighbouring rivers, the risk of extrinsic outbreeding depression may be high

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 early marine distribution of Atlantic salmon in the North-east Atlantic : A genetically informed stock-specific synthesis

The survival of Atlantic salmon (Salmo salar), an increasingly rare anadromous species, has declined dramatically during its marine phase, with disproportionate impacts on the poorly understood early post-smolt period. Logistical constraints on collecting oceanic data to inform this issue pose a formidable obstacle. To advance understanding of post-smolt distributional ecology in the North-east Atlantic, a comprehensive analysis of existing information was undertaken. Data were synthesized from 385 marine cruises, 10,202 individual trawls, and 9,269 captured post-smolts, spanning three decades and similar to 4.75 million km(2) of ocean, with 3,423 individuals genetically assigned to regional phylogeographic origin. The findings confirm major migrational post-smolt aggregations on the continental shelf-edge off Ireland, Scotland and Norway, and an important marine foraging area in the Norwegian Sea. Genetic analysis shows that aggregational stock composition does not simply reflect distance to natal rivers, with northern phylogeographic stock groups significantly under-represented in sampled high-seas aggregations. It identifies a key foraging habitat for southern European post-smolts located in international waters immediately west of the Voring Plateau escarpment, potentially exposing them to a high by-catch mortality from extra-territorial pelagic fisheries. Evidence of the differential distribution of regional stocks points to fundamental differences in their migration behaviours and may lead to inter-stock variation in responses to environmental change and marine survival. The study shows that understanding of post-smolt marine ecology, as regards to stock-specific variations in habitat utilization, biological performance and exposure to mortality factors, can be significantly advanced by data integration across studies and exploiting genetic approaches.Peer reviewe