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

Workshop on the production of swept-area estimates for all hauls in DATRAS for biodiver-sity assessments (WKSAE-DATRAS)

The workshop on the production of swept-area estimates for all hauls in DATRAS for biodiver-sity assessments (WKSAE-DATRAS) considered three groups of surveys for which data are sub-mitted to the Database of Trawl Surveys (DATRAS): various Beam Trawl Surveys, the Northeast Atlantic International Bottom Trawl Survey (Northeast Atlantic IBTS), and the North Sea Inter-national Bottom Trawl Survey (North Sea IBTS). All countries contributing to the above-mentioned surveys were represented by at least one par-ticipant during the workshop, apart from the Netherlands and Norway. The main objectives of the workshop were to establish tow-by-tow swept-area estimates for time-series as far back in time as possible, compare different approaches for the estimates of missing observations, and harmonize the resulting dataseries for biodiversity assessments. For all of the surveys considered, problems with data quality were detected. This included the Beam Trawl Surveys but was most pronounced for the North Sea IBTS. Outliers and potential erroneous data were listed for reporting back to the respective national institutes. In particular, missing observations or algorithms affected wing spread-based swept-area, which is needed in several applications. This workshop compared the Marine Scotland Science-MSS/OSPAR approach, which includes a data quality check for the information needed for the calculation of swept-area, and the DATRAS approach, which depends solely on correctly reported data from the national institutes. Larger data gaps were identified, in particular for several years of the North Sea IBTS. For those surveys, it is proposed that the best possible way forward at this moment is to use estimates based on the MSS/OSPAR approach. However, if dubious records (i.e. extreme outliers) were identified by the MSS/OSPAR and no other information was available, values (e.g. speed over ground or the depth at which a change from short to long sweeps should have happened) were taken from the manual. However, expe-rience has shown that the survey manuals are not followed in all instances, and so persistent country-specific and survey-specific deviations may occur. The national institutes are encouraged to check, correct, and fill in missing survey data through re-submissions to DATRAS. It is recommended that DATRAS data quality control on data sub-mission is extended for the information needed for the calculation of swept-area (e.g. distance, depth, door spread, and wing spread) and that this is done in close cooperation between the ICES Data Centre and the respective ICES survey working groups, WGBEAM (Working Group on Beam Trawl Surveys) and IBTSWG (International Bottom Trawl Survey Working Group).info:eu-repo/semantics/publishedVersio

Baltic International Fish Survey Working Group (WGBIFS)

The Baltic International Fish Survey Working Group (WGBIFS) plans, coordinates, and imple-ments demersal trawl surveys and hydroacoustic surveys in the Baltic Sea including the Baltic International Acoustic Survey (BIAS), the Baltic Acoustic Spring Survey (BASS), and the Baltic International Trawl Surveys (BITS) in the 1st and 4th quarter on an annual basis. The group com-piles results from these surveys and provides the herring, sprat, cod and flatfish abundance in-dices for the Baltic Fisheries Assessment Working Group (WGBFAS) to use as tuning fleets. In 2023, WGBIFS completed the following tasks: (1) compiled survey results from 2022 and the first half of 2023, (2) planned and coordinated all Baltic fish stocks assessment relevant surveys for the second half of 2023 and the first half of 2024, (3) updated the common survey manuals according to decisions made during the annual WGBIFS meeting. Data from the recent BITS was added to the ICES Database of Trawl Surveys (DATRAS). The Tow-Database was corrected and updated. The Access-databases for aggregated acoustic data and the ICES database of acoustic-trawl surveys for disaggregated data were updated. All countries registered collected litter ma-terials to DATRAS. The area coverage and the number of control hauls in the BASS, BIAS and GRAHS in 2022 were considered to be appropriate to the calculation of tuning indices and the data can be used for the assessment of Baltic herring and sprat stocks. The number of valid hauls accomplished during the 4th quarter 2022 and 1st quarter 2023 BITS were considered by the group as appropriate to tuning series and the data can be used for the assessment of Baltic and Kattegat cod and flatfish stocks. BIAS and BASS survey sampling variance calculation questions were discussed and standard deviation for Central Baltic herring acoustic index series calculated. In comparison exercises between the StoX survey computational method and traditional IBAS calculation methods it was found that the StoX project, developed for the WGBIFS, has small methodological differences compared to the standard calculation method used by the group, as specified in the Manual for the International Baltic Acoustic Surveys (IBAS), and is thereby caus-ing a small difference in the total number of herring and sprat., The work with transition to a more transparent calculation software (e.g. StoX) will continue during the next period with more thorough analysis of calculation methodologies. A further comparison exercise between the StoX method and traditional Gulf of Riga Herring Survey calculation method was performed using data from 11 last years. It showed no major differences in herring total abundance estimates for most of the years. However, notable differ-ences were in the age compositions of those two methods. Some errors and differences in input data (uploaded into the ICES database) were found and therefore the further analysis was post-poned until these issues are fixed. WGBIFS is planning to continue with analogical comparison exercises in the coming years before the final transition to a transparent reproducible pathway into the ICES Transparent Assessment Framework (TAF) can be done. Work towards transitioning to TAF will continue during the next 3-year period until all methodological and database differences are resolved. Inquiries from other ICES expert groups were discussed and addressed

The nature of different information sources

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Workshop on the production of swept-area estimates for all hauls in DATRAS for biodiversity assessments (WKSAE-DATRAS). ICES Scientific Reports, 3:74.

The workshop on the production of swept-area estimates for all hauls in DATRAS for biodiver-sity assessments (WKSAE-DATRAS) considered three groups of surveys for which data are sub-mitted to the Database of Trawl Surveys (DATRAS): various Beam Trawl Surveys, the Northeast Atlantic International Bottom Trawl Survey (Northeast Atlantic IBTS), and the North Sea Inter-national Bottom Trawl Survey (North Sea IBTS). All countries contributing to the above-mentioned surveys were represented by at least one par-ticipant during the workshop, apart from the Netherlands and Norway. The main objectives of the workshop were to establish tow-by-tow swept-area estimates for time-series as far back in time as possible, compare different approaches for the estimates of missing observations, and harmonize the resulting dataseries for biodiversity assessments. For all of the surveys considered, problems with data quality were detected. This included the Beam Trawl Surveys but was most pronounced for the North Sea IBTS. Outliers and potential erroneous data were listed for reporting back to the respective national institutes. In particular, missing observations or algorithms affected wing spread-based swept-area, which is needed in several applications. This workshop compared the Marine Scotland Science-MSS/OSPAR approach, which includes a data quality check for the information needed for the calculation of swept-area, and the DATRAS approach, which depends solely on correctly reported data from the national institutes. Larger data gaps were identified, in particular for several years of the North Sea IBTS. For those surveys, it is proposed that the best possible way forward at this moment is to use estimates based on the MSS/OSPAR approach. However, if dubious records (i.e. extreme outliers) were identified by the MSS/OSPAR and no other information was available, values (e.g. speed over ground or the depth at which a change from short to long sweeps should have happened) were taken from the manual. However, expe-rience has shown that the survey manuals are not followed in all instances, and so persistent country-specific and survey-specific deviations may occur. The national institutes are encouraged to check, correct, and fill in missing survey data through re-submissions to DATRAS. It is recommended that DATRAS data quality control on data sub-mission is extended for the information needed for the calculation of swept-area (e.g. distance, depth, door spread, and wing spread) and that this is done in close cooperation between the ICES Data Centre and the respective ICES survey working groups, WGBEAM (Working Group on Beam Trawl Surveys) and IBTSWG (International Bottom Trawl Survey Working Group)