Small scale fisheries (SSF) account for around 80% of the fishing fleet in the world. Yet, proper knowledge of their activity is scarce due to the difficulties in data collection compared to industrial fisheries. Despite the efforts to improve the understanding of SSF, significant limitations persist, particularly regarding their spatial distribution.
These restrictions hamper effective management for resource sustainability, marine ecosystem health, and local fishing communities. In addition, the emergence of the blue economy, needs on marine spatial planning and stricter environmental requirements demand an enhanced comprehension of the spatial activity of SSF. Some examples are those related to the implementation of MPAs, wind farms or the estimation of fisheries impacts to assess ecosystem status (as required, for instance, by the EU Strategic Marine Framework) among others.
Fortunately, an increasing number of countries are now embracing the monitoring of SSF activities using tracking devices. This holds significant potential in overcoming the challenges associated with data collection in SSF. Proper analysis from this data would identify and quantify the complexity of behaviours within SSF, which are characterized by a high diversification in gears and fishing techniques and changing spatio-temporal patterns of their use.
ICES community has accumulated expertise working with VMS. However, SSF requires highly-resolved spatial data, i.e, with higher sampling frequencies (GPS, AIS, EM, etc..), to be able to identify fishing activities which are usually shorter in time and space. In this context, emerging techniques as machine learning and other analytical tools are being tested to identify and model fishing operations and obtain the activity indicators (e.g. effort/soak time, bottom impact or bycatch).
This theme session provides an opportunity to review the latest advances in analysing highly-resolved spatial data. It will address topics including data collection technologies, data processing and management, and indicators/knowledge obtained from SSF geospatial data.
