AIS data to inform small scale fisheries management and marine spatial planning - paper.

 

Example of basic AIS data from VesselTracker.

The paper investigates the potential of using Automatic Identification Systems (AIS) data to inform small-scale fisheries management and marine spatial planning, specifically along the Scottish coast. AIS, originally designed for collision avoidance, broadcasts a vessel's position, track, and speed. By modeling AIS data reception and utilizing open-source Geographic Information System software, the study analyzes three months of AIS data from 274 small-scale fishing vessels. The research provides valuable insights into trip durations, distances traveled, and dependency on fishing grounds, while also addressing the challenges and opportunities presented by the uneven coverage of AIS receivers, especially in complex coastal topographies. The aim is to enhance understanding of fishing activities, inform management practices, and support the sustainable use of marine resources.

Paper by Tania Mendo  Publisher: Elsevier BV Publication

What are the main benefits of using AIS data for small-scale fisheries management? 

AIS data provides several benefits for small-scale fisheries management, including:

• Characterization of Fishing Trips: AIS data can help identify and analyze individual fishing trips by filtering out non-fishing movements, thus giving insights into actual fishing activities.

• Spatial Planning Support: The data's ability to illustrate the spatio-temporal distribution of fishing activities can inform marine spatial planning, aiding in the efficient allocation of marine resources and compliance with conservation policies.

• Integration with Other Data: The study suggests that integrating AIS data with other relevant metrics can improve fisheries management and enhance understanding of fisheries interactions with the environment.

What limitations of AIS data are highlighted in the study for regulatory purposes? 

The study identifies several limitations when using AIS data for regulatory purposes:

• Coverage Limitations: The distribution of AIS receivers is not uniform, and local topography can obstruct line-of-sight communication, compromising data reception in certain areas.

• Quality of Data: There may be issues with the quality of AIS transmissions that need careful consideration, including error rates in identifying actual fishing activities versus non-fishing movements.

• Need for Tailored Filtering Criteria: The effectiveness of identifying fishing trips depends on region-specific filtering criteria, which may need continuous refinement to accommodate variations in fishing practices and vessel operations.

How was the AIS data collected and analyzed for the research? 

The AIS data was collected from 274 small-scale fishing vessels operating within Scottish Territorial waters over three months. The researchers used open-source Geographic Information System and relational database software to model AIS data reception and conduct analyses. Computationally efficient methods were explored to process the large data volumes, focusing on spatio-temporal analyses of fishing trips, including duration, distance traveled, and fishing ground dependency. The methodology involved applying specific filtering criteria to discern fishing trips from other vessel movements, allowing for a detailed understanding of fishing activities in the area.