Feasibility study into a scientific self-sampling programme for the pelagic sector: FIS020

Fishermen’s perceptions of the shortcomings of scientific surveys and sampling schemes contribute to their lack of trust in the reliability of fish stock assessments. At the same time, scientists doubts about the reliability of catch data are responsible for a large degree of uncertainty in stock assessments. There are opportunities to improve both trust and data quality from both sides.
Taking new responsibilities for providing scientific data through self-sampling is seen by fishermen as a welcome opportunity to directly contribute to the continuous improvement of stock assessments. Experience shows that successful self-sampling schemes rely on effective feedback to fishermen, particularly in relation to what their data shows and how it is being used. This feedback helps to improve confidence in science and management, and reinforces effective collaboration between industry, science and management on achieving sustainable and profitable fisheries. Using paper diaries and electronic plotter devices, Scottish pelagic fishermen already record substantial quantities of data that describe where and when they fished, what they caught, and in some cases, environmental and biological information. They are willing
and have the capability and capacity to do more. The pelagic industry lends itself to a selfsampling programme because pelagic fishermen want to engage with science; have a direct stake in the information they generate; are capable and early adopters of new innovations; and they have the means for a well-organised and managed implementation. The purpose of this report is to identify opportunities for the Scottish pelagic industry to collect and contribute relevant data to support the assessment of stocks and management of fisheries. In doing so, it describes the requirements of a scientific self-sampling programme and what such a programme might look like. It also discusses how selfsampling schemes might help to address information needs in less data rich situations,
such as those in demersal and Nephrops fisheries. The four vital elements of effective self-sampling programmes are: (1) matching data opportunities with incentives that create a lasting ‘want to’ attitude, (2) establishing practical processes that can be efficiently implemented to a high quality standard, (3)
feedback on progress and results, and (4) achieving the intended impact.
The design process starts with having a clear view of what data are needed and how they can be used, so that any data provided by industry has the best possible chance of being used in scientific and management applications. Table 2.2 identifies scientific and management information needs and maps these needs onto a wide range of potential data contributions from a pelagic self-sampling programme. Table 3.1 is more specific, identifying the data provision opportunities and their scientific applications for mackerel, herring and blue whiting. For all three species, the collection of biological data for every haul can provide benefits to science, management and business applications. Critically, it can provide the means to help evaluate the performance of current sampling activities, which is a starting point to identifying any gaps, biases and uncertainties that may benefit from improvement. For mackerel, the principal gains relate to the quality and resolution of data and evidence of the spatial distribution of fish and fishing. The same points apply to herring and blue
whiting, but there are additional opportunities to provide relevant data describing stock structure. It is particularly important for blue whiting, where sampling is very low but the importance of the fishery to Scotland has been increasing recently. The architecture, or design, of a pelagic self-sampling programme is presented graphically in Section 7, with a specific example given for mackerel. Implementation of the practical sampling methods on vessels should be relatively straightforward, with developments in efficient electronic recording and data capture systems playing an important part in the future. Sampling at factories offers an alternative way to obtain a range of useful scientific data through minor adaptations to existing quality control
sampling procedures. In both situations, a central challenge will be ensuring that any industry-led sampling programme can be maintained over a period that is long enough to demonstrate its value to science and management.
The cost of time associated with collecting data at sea or at factories would be absorbed in to the daily operations of vessels and factories. Similarly, the industry would bear the costs for oversight of a self-sampling programme. Additional cost and effort from scientists would be necessary where specialist tasks such as age-reading of otoliths and data storage/ handling functions are required. Options for supporting these requirements, such as utilisation of scientific quota, an industry-science levy and project funding need to be discussed as a necessary next step. Further discussion on training needs is also required. Like the pelagic sector, greater engagement of the demersal and Nephrops sectors in selfsampling schemes would be beneficial in a number of ways such as: quantifying effects of the landing obligation and identifying mitigation measures, filling biological information gaps (e.g. for Data Limited Stocks), providing samples for stock identification studies, aiding scientific survey planning and verifying perceptions in the changes in abundance
and distribution of stocks. Although the size and diversity of the demersal and Nephrops sectors presents numerous challenges in implementation, there are opportunities for self-sampling programmes to routinely deliver scientifically valuable data. The architecture outlined here for the pelagic sector, is a useful guide to exploring in more detail the opportunities outlined in this report.