Integrating Knowledge: SELF-SAMPLING
Tool authored by: SašaRaicevich and Marloes Kraan

Tool Description

In recent years self-sampling has been seen as a very promising approach to achieve a more detailed knowledge base on fisheries catches and discards, since it allows for sampling with a higher coverage (in space and time) and allows logistical and economic restrictions usually associated to the collection of data through observers or research vessels to be overcome (ICES, 2008; Mangi et al., 2013).

Self-sampling is an activity carried out by fishers gathering data/samples during their fishing trips. This method is closely linked to participatory sampling, since its general setting should be conceived in the framework of participatory activities. In this case fishers have a prominent role in the collecting of samples/data. Indeed, the full responsibility of the collection of data/samples relies on fishers, who should apply a methodological approach jointly defined with scientists. It is often necessary to train the fishers involved in how to apply the methodology correctly. Self-sampling should be embedded in collaborative research projects which are organised in such a way that enough attention can be given to communication, interpretation and mutual learning (Johnson& Van Densen 2009). This implies the necessity to establish mutual trust, since the quality of data is related to the willingness to collect representative data and avoid misreporting. Collected data are then shared with researchers, who should also involve fishers in data analysis and interpretation (Kraan et al., 2013).

Expected Outcomes 
The outcomes of self-sampling activities are a range of data. In particular, it is expected that self-sampling will readily contribute to expand the knowledge base used for fisheries management, providing data that will be trusted by both fishers and scientist. Joint analyses and discussion of results will also increase the saliency, legitimacy and credibility of the information.

What is needed

A first need is the establishment of a participatory framework to generate mutual trust, collaboration and ensure the setting of common goals. This activity should allow the definition of the variables/samples to be collected by fishers, the detailed methodology to be applied, as well as the training of fishers. Materials for data/samples collection should be prepared and made available, along with protocols and reporting sheets for sharing data.

How it works

• There is the need to first establish a participatory framework between scientists and stakeholders to define common objectives of self-sampling. This process could also include stakeholders, other scientists (assessment biologists for instance) and policy makers as potential end users of the collected data;
• An agreement of the usage of data should be also achieved in this phase,
• The methodologies should be jointly defined by the group, along with methodological standards and protocols for data/samples collection;
• Training activities and mutual learning events should be carried out to ensure the necessary skills are available for the collection of data;
• It would be suggested that researchers join field trips in the beginning of self-sampling activities to provide guidance when needed;
• Throughout the duration of the sampling activities it would be useful to establish a parallel observer program to test the reliability and potential sources of bias in collected data and related procedures;
• The communication between fishers and scientists should continue throughout the sampling period, along with the provision of early and intermediate results to maintain the involvement of fishers;
• Collected data should be jointly analyzed and/or discussed with fishers, to ensure the interpretation is agreed (each with their own expertise) and results are fully understood.
• It is advisable that the dilemmas of self-sampling are openly discussed between the different parties (Kraan et al 2013).

Warnings

• It is necessary to provide fishers with the skills to collect data/samples through training activities;
• An agreement on the use of collected data and samples should be defined prior to the beginning of data collection;
• Parallel observers program should be established to assess data reliability and assess potential sources of bias in self-sampled data (Roman et al., 2011);
• Interim reports on the data collected should be given to fishers while self-sampling, to provide feedback and stimulate their continuous collaboration.
• It is necessary to closely monitor the data provided by the fishermen and to give rapid feedback on the quality.

Examples from GAP2 and beyond

In the Netherlands, the pros and cons of self-sampling were investigated by Dr. Marloes Kraan in the context of GAP2, taking advantage from on-going programs on the self-sampling of discards established in the area. The self-sampling activity greatly expanded the knowledge base used to assess discards in the DCF context. The introduction of the method was inspired by the critique on the reliability of official data used for fisheries management (more at: http://gap2.eu/case-studies/case-study-7).

In Italy, Dr.Saša Raicevich and colleagues involved fishers in the self-sampling of catch data through the use of an electronic-logbook connected to a GPS, installed on board the fishing vessels. The skippers and the crews of 7 fishing vessels (5 otter-trawlers and 2 beam trawlers) joined this effort, collecting catch data from September 2012 to December 2014, covering around 4800 hauls, for eight target species. The species to be investigated were jointly defined by fishers and scientists. A parallel independent observer program showed the high quality of self-sampled data and gathered auxiliary biological data (i.e. length frequency distribution). Fishers also contributed to the improvement of the electronic logbook software providing suggestions and requests for its update. Moreover, they were involved in data interpretation providing feedback on the reliability of the outcomes of the data analyses. Such activity allowed to reconstruct the spatio-temporal distribution and life-cycle of targeted species in the Northern Adriatic Sea (more at: http://gap2.eu/case-studies/case-study-8).

In Galicia, fishers collaborated with Dr. Pablo Pita Orduna and his team sharing logbook data to describe the spatial distribution of fishing effort and catches at high spatial resolution. These data were used for comparison with data collected by means of participatory mapping activities (see related section in this report) (more at: http://gap2.eu/case-studies/case-study-2)

What people say about this tool

“For the first time we have data at high resolution to describe the species life-cycle in the Northern Adriatic Sea. Fishers were very collaborative in collecting and sharing data, and our analyses showed that they collected reliable and high quality data. Without having jointly defined the objectives of self-sampling and established mutual trust, we would have never achieved such a relevant outcome” (Dr.Saša Raicevich, ISPRA researcher, Italy)

“For the first time I see data and maps that are really representing the complex life-cycle of the fish as we know them. Yes, these are our data but the analyses and synthesis made by scientists fully agree with my knowledge, I wish these data to be used for setting management rules in the Adriatic Sea” (Mr.Renzo Zennaro, Italian fisher)

“Its only after sampling myself I realized that we actually do have a lot of discards” (Mr. Van Urk, Dutch fisher).

References and resources

ICES. 2008. Report of the Workshop on Fishers Sampling of Catches (WKSC), 10–13 June 2008, ICES, Copenhagen, Denmark. ICES CM 2008/ACOM:30. 61 pp.

Kraan, M., Uhlmann, S., Steenbergen, J., Van Helmond, A.T.M., Van Hoof, L., 2013. The optimal process of self-sampling in fisheries: lessons learned in the Netherlands. Journal of Fish Biology 83 (4), 963–973.

Mangi S.C., Dolder P.J., Catchpole T.L., Rodmel D., de Rozarieux N. (2013). Approaches to fully documented fisheries: practical issues and stakeholder perceptions. Fih and Fisheries, DOI: 10.1111/faf.12065

Roman s., Jacobson N., Cadrin S. (2011). Assessing the Reliability of Fisher Self-Sampling Programs’, North American Journal of Fisheries Management, 31: 1, 165 —175

Johnson, T. R. & van Densen, W. L. T. (2007). The benefits and organization of cooperative research for fisheries management. ICES Journal of Marine Science 64, 834–840. doi: 10.1093/icesjms/fsm014

Web resources

• IMARES is developing a self-sampling guide (in English), which will be available soon, please check on their website.

Visual references and resources