Description: This is the first volume of the entirely rewritten, revised and updated version of the original FAO Catalogue of Cephalopods of the World (1984). The present Volume is a multiauthored compilation that reviews six families: Nautilidae, Sepiidae, Sepiolidae, Sepiadariidae, Idiosepiidae and Spirulidae, with 23 genera and the 201 species known to the date of the completion of the volume. It provides accounts for all families and genera, as well as illustrated keys to all taxa. Information under each species account includes: valid modern systematic name and original citation of the species (or subspecies); main synonyms; English, French and Spanish FAO names for the species; illustrations of dorsal and ventral aspect of the whole animal (as necessary) and other distinguishing illustrations; field characteristics; diagnostic features; geographic and vertical distribution, including GIS map; size; habitat; biology; interest to fishery; local names when available; a remarks section (as necess ary) and literature. The volume is fully indexed and also includes sections on terminology and measurements, an extensive glossary, an introduction with an updated review of the existing biological knowledge on cephalopods (including fisheries information and catch data for recent years) and a dedicated bibliography.

Description: This is the second volume of the entirely rewritten, revised and updated version of the original FAO Catalogue of Cephalopods of the World (1984). The present Volume is a multiauthored compilation that reviews 28 families, i.e. (in alphabetical order), Ancistrocheiridae, Architeuthidae, Australiteuthidae, Bathyteuthidae, Batoteuthidae, Brachioteuthidae, Chiroteuthidae, Chtenopterygidae, Cranchiidae, Cycloteuthidae, Enoploteuthidae, Gonatidae, Histioteuthidae, Joubiniteuthidae, Lepidoteut hidae, Loliginidae, Lycoteuthidae, Magnapinnidae, Mastigoteuthidae, Neoteuthidae, Octopoteuthidae, Ommastrephidae, Onychoteuthidae, Pholidoteuthidae, Promachoteuthidae, Psychroteuthidae, Pyroteuthidae and Thysanoteuthidae, with 83 genera and the 295 species known and named to the date of the completion of the volume. It provides accounts for all families and genera, as well as illustrated keys. Information under species accounts includes: valid modern systematic name and original citatio n of the species (or subspecies); synonyms; English, French and Spanish FAO names for the species; illustrations of dorsal and ventral aspects of the whole animal (as necessary) and other distinguishing illustrations; field characteristics; diagnostic features; geographic and vertical distribution, including GISmap; size; habitat; biology; interest to fishery; local names when available; a remarks section (as necessary) and literature. The Volume is fully indexed and also includes sectio ns on terminology and measurements, an extensive glossary, an introduction with an updated review of the existing biological knowledge on squids (including fisheries information andmain catch data for recent years) and a dedicated bibliography. Due to the conspicuous amount of literature addressingmany squid species, an appendix is included in the online version, where those references considered most pertinent to the species are listed, by family and species, in alphabetical order by au thor; key words, also, are reported.

Description: Over the past two decades, cephalopod molluscs have attracted increased attention from marine biologists and fishery scientists. Several species are important for European fisheries, as targets of small‐scale coastal fisheries and/or as bycatch in multispecies fisheries for demersal fish. The present report draws on a series of reviews prepared in 2005 for the CEPHSTOCK project (see Section 1). The taxonomy of the main resource species is reviewed (Section 2), and brief descriptions of each species are provided, along with information from studies of population genetics, habitat requirements of paralarvae and adults, and health and ecotoxicology (Section 3). The main fisheries are described, including illustration of gears used in specialized small‐scale fisheries and a discussion of the socio‐economic importance of the fisheries. The current status of cephalopod aquaculture is reviewed, highlighting notable advances in commercial culture of octopus and cuttlefish (Section 4). Current fishery data collection and fishery management are described, noting that there is no setting of landings quotas and no routine assessment of stock status. Options for stock assessment are discussed, drawing on one‐off assessments made during specific projects and current practice elsewhere in the world. The “live fast, die young” lifehistory strategies of cephalopods present particular challenges, but parallels can be drawn with short‐lived fish (Section 5). Finally, the report looks to the future, reviewing possible effects of climate change on cephalopods. It discusses the future development of aquaculture and fisheries for cephalopods, including prospects for fishery forecasting and fishery management – especially in relation to the small‐scale directed fisheries. Various knowledge gaps are identified, and ideas for research to fill these gaps are presented.

Description: Marine geohazards pose a significant threat to the European coastal population and to the development of the Blue Economy. This Position Paper discusses the type, distribution and impact of marine geohazards on the European coastal regions and the Blue Economy, as well as what and how novel scientific approaches may broaden our understanding of their trigger mechanisms and drive a risk-mitigating European policy.

Description: 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.